A comparison of plan-based and abstract MDP reward shaping

NASA Astrophysics Data System (ADS)

Efthymiadis, Kyriakos; Kudenko, Daniel

2014-01-01

Reward shaping has been shown to significantly improve an agent's performance in reinforcement learning. As attention is shifting away from tabula-rasa approaches many different reward shaping methods have been developed. In this paper, we compare two different methods for reward shaping; plan-based, in which an agent is provided with a plan and extra rewards are given according to the steps of the plan the agent satisfies, and reward shaping via abstract Markov decision process (MDPs), in which an abstract high-level MDP of the environment is solved and the resulting value function is used to shape the agent. The comparison is conducted in terms of total reward, convergence speed and scaling up to more complex environments. Empirical results demonstrate the need to correctly select and set up reward shaping methods according to the needs of the environment the agents are acting in. This leads to the more interesting question, is there a reward shaping method which is universally better than all other approaches regardless of the environment dynamics?

Recommendation System Based On Association Rules For Distributed E-Learning Management Systems

NASA Astrophysics Data System (ADS)

Mihai, Gabroveanu

2015-09-01

Traditional Learning Management Systems are installed on a single server where learning materials and user data are kept. To increase its performance, the Learning Management System can be installed on multiple servers; learning materials and user data could be distributed across these servers obtaining a Distributed Learning Management System. In this paper is proposed the prototype of a recommendation system based on association rules for Distributed Learning Management System. Information from LMS databases is analyzed using distributed data mining algorithms in order to extract the association rules. Then the extracted rules are used as inference rules to provide personalized recommendations. The quality of provided recommendations is improved because the rules used to make the inferences are more accurate, since these rules aggregate knowledge from all e-Learning systems included in Distributed Learning Management System.

Content-based Music Search and Recommendation System

NASA Astrophysics Data System (ADS)

Takegawa, Kazuki; Hijikata, Yoshinori; Nishida, Shogo

Recently, the turn volume of music data on the Internet has increased rapidly. This has increased the user's cost to find music data suiting their preference from such a large data set. We propose a content-based music search and recommendation system. This system has an interface for searching and finding music data and an interface for editing a user profile which is necessary for music recommendation. By exploiting the visualization of the feature space of music and the visualization of the user profile, the user can search music data and edit the user profile. Furthermore, by exploiting the infomation which can be acquired from each visualized object in a mutually complementary manner, we make it easier for the user to search music data and edit the user profile. Concretely, the system gives to the user an information obtained from the user profile when searching music data and an information obtained from the feature space of music when editing the user profile.

Recommender systems in knowledge-mining

NASA Astrophysics Data System (ADS)

Volna, Eva

2017-07-01

The subject of the paper is to analyse the possibilities of application of recommender systems in the field of data mining. The work focuses on three basic types of recommender systems (collaborative, content-based and hybrid). The goal of the article is to evaluate which of these three concepts of recommender systems provides forecast with the lowest error rate in the domain of recommending movies. This target is fulfilled by the practical part of the work - at first, the own recommender system was designed and created, capable of obtaining movies recommendation from the database based on the user's preferences. Next, we verified experimentally which recommender system produces more accurate results.

Distributed Deliberative Recommender Systems

NASA Astrophysics Data System (ADS)

Recio-García, Juan A.; Díaz-Agudo, Belén; González-Sanz, Sergio; Sanchez, Lara Quijano

Case-Based Reasoning (CBR) is one of most successful applied AI technologies of recent years. Although many CBR systems reason locally on a previous experience base to solve new problems, in this paper we focus on distributed retrieval processes working on a network of collaborating CBR systems. In such systems, each node in a network of CBR agents collaborates, arguments and counterarguments its local results with other nodes to improve the performance of the system's global response. We describe D2ISCO: a framework to design and implement deliberative and collaborative CBR systems that is integrated as a part of jcolibritwo an established framework in the CBR community. We apply D2ISCO to one particular simplified type of CBR systems: recommender systems. We perform a first case study for a collaborative music recommender system and present the results of an experiment of the accuracy of the system results using a fuzzy version of the argumentation system AMAL and a network topology based on a social network. Besides individual recommendation we also discuss how D2ISCO can be used to improve recommendations to groups and we present a second case of study based on the movie recommendation domain with heterogeneous groups according to the group personality composition and a group topology based on a social network.

Recruitment recommendation system based on fuzzy measure and indeterminate integral

NASA Astrophysics Data System (ADS)

Yin, Xin; Song, Jinjie

2017-08-01

In this study, we propose a comprehensive evaluation approach based on indeterminate integral. By introducing the related concepts of indeterminate integral and their formulas into the recruitment recommendation system, we can calculate the suitability of each job for different applicants with the defined importance for each criterion listed in the job advertisements, the association between different criteria and subjective assessment as the prerequisite. Thus we can make recommendations to the applicants based on the score of the suitability of each job from high to low. In the end, we will exemplify the usefulness and practicality of this system with samples.

Semantically Enhanced Recommender Systems

NASA Astrophysics Data System (ADS)

Ruiz-Montiel, Manuela; Aldana-Montes, José F.

Recommender Systems have become a significant area in the context of web personalization, given the large amount of available data. Ontologies can be widely taken advantage of in recommender systems, since they provide a means of classifying and discovering of new information about the items to recommend, about user profiles and even about their context. We have developed a semantically enhanced recommender system based on this kind of ontologies. In this paper we present a description of the proposed system.

Hot news recommendation system from heterogeneous websites based on bayesian model.

PubMed

Xia, Zhengyou; Xu, Shengwu; Liu, Ningzhong; Zhao, Zhengkang

2014-01-01

The most current news recommendations are suitable for news which comes from a single news website, not for news from different heterogeneous news websites. Previous researches about news recommender systems based on different strategies have been proposed to provide news personalization services for online news readers. However, little research work has been reported on utilizing hundreds of heterogeneous news websites to provide top hot news services for group customers (e.g., government staffs). In this paper, we propose a hot news recommendation model based on Bayesian model, which is from hundreds of different news websites. In the model, we determine whether the news is hot news by calculating the joint probability of the news. We evaluate and compare our proposed recommendation model with the results of human experts on the real data sets. Experimental results demonstrate the reliability and effectiveness of our method. We also implement this model in hot news recommendation system of Hangzhou city government in year 2013, which achieves very good results.

Hot News Recommendation System from Heterogeneous Websites Based on Bayesian Model

PubMed Central

Xia, Zhengyou; Xu, Shengwu; Liu, Ningzhong; Zhao, Zhengkang

2014-01-01

The most current news recommendations are suitable for news which comes from a single news website, not for news from different heterogeneous news websites. Previous researches about news recommender systems based on different strategies have been proposed to provide news personalization services for online news readers. However, little research work has been reported on utilizing hundreds of heterogeneous news websites to provide top hot news services for group customers (e.g., government staffs). In this paper, we propose a hot news recommendation model based on Bayesian model, which is from hundreds of different news websites. In the model, we determine whether the news is hot news by calculating the joint probability of the news. We evaluate and compare our proposed recommendation model with the results of human experts on the real data sets. Experimental results demonstrate the reliability and effectiveness of our method. We also implement this model in hot news recommendation system of Hangzhou city government in year 2013, which achieves very good results. PMID:25093207

The effect of continuous application of MDP-containing primer and luting resin cement on bond strength to tribochemical silica-coated Y-TZP.

PubMed

Lim, Myung-Jin; Yu, Mi-Kyung; Lee, Kwang-Won

2018-05-01

This study investigated the effect of continuous application of 10-methacryloyloxydecyldihydrogen phosphate (MDP)-containing primer and luting resin cement on bond strength to tribochemical silica-coated yttria-stabilized tetragonal zirconia polycrystal (Y-TZP). Forty bovine teeth and Y-TZP specimens were prepared. The dentin specimens were embedded in molds, with one side of the dentin exposed for cementation with the zirconia specimen. The Y-TZP specimen was prepared in the form of a cylinder with a diameter of 3 mm and a height of 10 mm. The bonding surface of the Y-TZP specimen was sandblasted with silica-coated aluminium oxide particles. The forty tribochemical silica-coated Y-TZP specimens were cemented to the bovine dentin (4 groups; n = 10) with either an MDP-free primer or an MDP-containing primer and either an MDP-free resin cement or an MDP-containing resin cement. After a shear bond strength (SBS) test, the data were analyzed using 1-way analysis of variance and the Tukey test (α = 0.05). The group with MDP-free primer and resin cement showed significantly lower SBS values than the MDP-containing groups ( p < 0.05). Among the MDP-containing groups, the group with MDP-containing primer and resin cement showed significantly higher SBS values than the other groups ( p < 0.05). The combination of MDP-containing primer and luting cement following tribochemical silica coating to Y-TZP was the best choice among the alternatives tested in this study.

Intravitreal injection of (99)Tc-MDP inhibits the development of laser-induced choroidal neovascularization in rhesus monkeys.

PubMed

Lai, Kunbei; Jin, Chenjin; Tu, Shu; Xiong, Yunfan; Huang, Rui; Ge, Jian

2014-07-01

The aim of this study was to investigate the safety and efficacy of intravitreal injection of (99)Tc-MDP, a decay product of (99m)Tc-MDP, on the development of laser-induced choroidal neovascularization (CNV) in rhesus monkeys. Experimental CNV was induced by argon laser with a small high-energy laser spot. Monkeys were given 50 μL of (99)Tc-MDP at a concentration of 0.005 μg/mL (n = 6) or 0.01 μg/mL (n = 6) by intravitreal injection once a week immediately after laser injury for a period of 56 days. Control animals were treated with the same volume of PBS (n = 6) in the same way. Eyes were monitored by ophthalmic examination, color fundus photography, fluorescence fundus angiography (FFA), optical coherence tomography (OCT) and histology. Incidences of grade 4 CNV lesions as well as the leakage areas of grade 4 CNVs on the late-phase of fluorescein angiograms were measured in a standardized, randomized and masked fashion fortnightly. The maximum widths and heights of grade 4 CNVs were also calculated by histology at the end of the experiment. Toxicity of (99)Tc-MDP on the retina was evaluated by electroretinogram (ERG) and histologic analysis. (99)Tc-MDP reduced the incidences of grade 4 CNVs by 33.33 % and 39.40 % in the 0.005 μg/mL and 0.01 μg/mL groups, respectively, compared with the PBS group on day 28 (P < 0.05; n = 6). The leakage areas of grade 4 CNVs were smaller in the 0.005 μg/mL (0.7136 ± 0.0283 mm(2), p <0.01; n = 6) and 0.01 μg/mL (0.4351 ± 0.0349 mm(2), p < 0.01; n = 6) groups than those in the PBS control group (0.9373 ± 0.0455 mm(2); n = 6) in a dose-dependent manner on day 28. OCT and histology also showed that the sizes of CNVs were smaller in the (99)Tc-MDP treated groups than those in the PBS group. Although intravitreal injection of (99)Tc-MDP led to mild inflammatory reaction in the anterior chamber, histology and ERG findings demonstrated that (99)Tc-MDP did not cause any change in

A Collaborative Location Based Travel Recommendation System through Enhanced Rating Prediction for the Group of Users.

PubMed

Ravi, Logesh; Vairavasundaram, Subramaniyaswamy

2016-01-01

Rapid growth of web and its applications has created a colossal importance for recommender systems. Being applied in various domains, recommender systems were designed to generate suggestions such as items or services based on user interests. Basically, recommender systems experience many issues which reflects dwindled effectiveness. Integrating powerful data management techniques to recommender systems can address such issues and the recommendations quality can be increased significantly. Recent research on recommender systems reveals an idea of utilizing social network data to enhance traditional recommender system with better prediction and improved accuracy. This paper expresses views on social network data based recommender systems by considering usage of various recommendation algorithms, functionalities of systems, different types of interfaces, filtering techniques, and artificial intelligence techniques. After examining the depths of objectives, methodologies, and data sources of the existing models, the paper helps anyone interested in the development of travel recommendation systems and facilitates future research direction. We have also proposed a location recommendation system based on social pertinent trust walker (SPTW) and compared the results with the existing baseline random walk models. Later, we have enhanced the SPTW model for group of users recommendations. The results obtained from the experiments have been presented.

A Flexible Electronic Commerce Recommendation System

NASA Astrophysics Data System (ADS)

Gong, Songjie

Recommendation systems have become very popular in E-commerce websites. Many of the largest commerce websites are already using recommender technologies to help their customers find products to purchase. An electronic commerce recommendation system learns from a customer and recommends products that the customer will find most valuable from among the available products. But most recommendation methods are hard-wired into the system and they support only fixed recommendations. This paper presented a framework of flexible electronic commerce recommendation system. The framework is composed by user model interface, recommendation engine, recommendation strategy model, recommendation technology group, user interest model and database interface. In the recommender strategy model, the method can be collaborative filtering, content-based filtering, mining associate rules method, knowledge-based filtering method or the mixed method. The system mapped the implementation and demand through strategy model, and the whole system would be design as standard parts to adapt to the change of the recommendation strategy.

Altered [99mTc]Tc-MDP biodistribution from neutron activation sourced 99Mo.

PubMed

Demeter, Sandor; Szweda, Roman; Patterson, Judy; Grigoryan, Marine

2018-01-01

Given potential worldwide shortages of fission sourced 99 Mo/ 99m Tc medical isotopes there is increasing interest in alternate production strategies. A neutron activated 99 Mo source was utilized in a single center phase III open label study comparing 99m Tc, as 99m Tc Methylene Diphosphonate ([ 99m Tc]Tc-MDP), obtained from solvent generator separation of neutron activation produced 99 Mo, versus nuclear reactor produced 99 Mo (e.g., fission sourced) in oncology patients for which an [ 99m Tc]Tc-MDP bone scan would normally have been indicated. Despite the investigational [ 99m Tc]Tc-MDP passing all standard, and above standard of care, quality assurance tests, which would normally be sufficient to allow human administration, there was altered biodistribution which could lead to erroneous clinical interpretation. The cause of the altered biodistribution remains unknown and requires further research.

A Collaborative Location Based Travel Recommendation System through Enhanced Rating Prediction for the Group of Users

PubMed Central

Ravi, Logesh; Vairavasundaram, Subramaniyaswamy

2016-01-01

Rapid growth of web and its applications has created a colossal importance for recommender systems. Being applied in various domains, recommender systems were designed to generate suggestions such as items or services based on user interests. Basically, recommender systems experience many issues which reflects dwindled effectiveness. Integrating powerful data management techniques to recommender systems can address such issues and the recommendations quality can be increased significantly. Recent research on recommender systems reveals an idea of utilizing social network data to enhance traditional recommender system with better prediction and improved accuracy. This paper expresses views on social network data based recommender systems by considering usage of various recommendation algorithms, functionalities of systems, different types of interfaces, filtering techniques, and artificial intelligence techniques. After examining the depths of objectives, methodologies, and data sources of the existing models, the paper helps anyone interested in the development of travel recommendation systems and facilitates future research direction. We have also proposed a location recommendation system based on social pertinent trust walker (SPTW) and compared the results with the existing baseline random walk models. Later, we have enhanced the SPTW model for group of users recommendations. The results obtained from the experiments have been presented. PMID:27069468

Muramyl dipeptide (MDP) induces reactive oxygen species (ROS) generation via the NOD2/COX-2/NOX4 signaling pathway in human umbilical vein endothelial cells (HUVECs).

PubMed

Kong, Ling-Jun; Liu, Xiao-Qian; Xue, Ying; Gao, Wei; Lv, Qian-Zhou

2018-03-20

Vascular endothelium dysfunction caused by oxidative stress accelerates the pathologic process of cardiovascular diseases. NOD2, an essential receptor of innate immune system, has been demonstrated to play a critical role in atherosclerosis. Here, the aim of our study was to investigate the effect and underlying molecular mechanism of muramyl dipeptide (MDP) on NOX4-mediated ROS generation in human umbilical vein endothelial cells (HUVECs). 2,7-dichlorofluorescein diacetate staining was to measure the intracellular ROS level and showed MDP promoted ROS production in a time- and dose-dependent manner. The mRNA and protein levels of NOX4 and COX-2 were detected by real-time PCR and western blot. Small interfering RNA (siRNA) was used to silence NOD2 or COX-2 gene expression and investigate the mechanism of NOD2-mediated signaling pathway in HUVECs. Data showed that MDP induced NOX4 and COX-2 expression in a time- and dose-dependent manner. NOD2 knock-down suppressed up-regulation of COX-2 and NOX4 in HUVECs treated with MDP. Furthermore, silence of COX-2 in HUVECs down-regulated the NOX4 expression after MDP stimulation. Collectively, we indicated that NOD2 played a leading role in MDP-induced COX-2/NOX4/ROS signaling pathway in HUVECs, which was a novel regulatory mechanism in the progress of ROS generation.

A Hybrid Approach using Collaborative filtering and Content based Filtering for Recommender System

NASA Astrophysics Data System (ADS)

Geetha, G.; Safa, M.; Fancy, C.; Saranya, D.

2018-04-01

In today’s digital world, it has become an irksome task to find the content of one's liking in an endless variety of content that are being consumed like books, videos, articles, movies, etc. On the other hand there has been an emerging growth among the digital content providers who want to engage as many users on their service as possible for the maximum time. This gave birth to the recommender system comes wherein the content providers recommend users the content according to the users’ taste and liking. In this paper we have proposed a movie recommendation system. A movie recommendation is important in our social life due to its features such as suggesting a set of movies to users based on their interest, or the popularities of the movies. In this paper we are proposing a movie recommendation system that has the ability to recommend movies to a new user as well as the other existing users. It mines movie databases to collect all the important information, such as, popularity and attractiveness, which are required for recommendation. We use content-based and collaborative filtering and also hybrid filtering, which is a combination of the results of these two techniques, to construct a system that provides more precise recommendations concerning movies.

Effect of a functional monomer (MDP) on the enamel bond durability of single-step self-etch adhesives.

PubMed

Tsuchiya, Kenji; Takamizawa, Toshiki; Barkmeier, Wayne W; Tsubota, Keishi; Tsujimoto, Akimasa; Berry, Thomas P; Erickson, Robert L; Latta, Mark A; Miyazaki, Masashi

2016-02-01

The present study aimed to determine the effect of the functional monomer, 10-methacryloxydecyl dihydrogen phosphate (MDP), on the enamel bond durability of single-step self-etch adhesives through integrating fatigue testing and long-term water storage. An MDP-containing self-etch adhesive, Clearfil Bond SE ONE (SE), and an experimental adhesive, MDP-free (MF), which comprised the same ingredients as SE apart from MDP, were used. Shear bond strength (SBS) and shear fatigue strength (SFS) were measured with or without phosphoric acid pre-etching. The specimens were stored in distilled water for 24 h, 6 months, or 1 yr. Although similar SBS and SFS values were obtained for SE with pre-etching and for MF after 24 h of storage in distilled water, SE with pre-etching showed higher SBS and SFS values than MF after storage in water for 6 months or 1 yr. Regardless of the pre-etching procedure, SE showed higher SBS and SFS values after 6 months of storage in distilled water than after 24 h or 1 yr. To conclude, MDP might play an important role in enhancing not only bond strength but also bond durability with respect to repeated subcritical loading after long-term water storage. © 2015 Eur J Oral Sci.

Hybrid context aware recommender systems

NASA Astrophysics Data System (ADS)

Jain, Rajshree; Tyagi, Jaya; Singh, Sandeep Kumar; Alam, Taj

2017-10-01

Recommender systems and context awareness is currently a vital field of research. Most hybrid recommendation systems implement content based and collaborative filtering techniques whereas this work combines context and collaborative filtering. The paper presents a hybrid context aware recommender system for books and movies that gives recommendations based on the user context as well as user or item similarity. It also addresses the issue of dimensionality reduction using weighted pre filtering based on dynamically entered user context and preference of context. This unique step helps to reduce the size of dataset for collaborative filtering. Bias subtracted collaborative filtering is used so as to consider the relative rating of a particular user and not the absolute values. Cosine similarity is used as a metric to determine the similarity between users or items. The unknown ratings are calculated and evaluated using MSE (Mean Squared Error) in test and train datasets. The overall process of recommendation has helped to personalize recommendations and give more accurate results with reduced complexity in collaborative filtering.

Evaluating Recommendation Systems

NASA Astrophysics Data System (ADS)

Shani, Guy; Gunawardana, Asela

Recommender systems are now popular both commercially and in the research community, where many approaches have been suggested for providing recommendations. In many cases a system designer that wishes to employ a recommendation system must choose between a set of candidate approaches. A first step towards selecting an appropriate algorithm is to decide which properties of the application to focus upon when making this choice. Indeed, recommendation systems have a variety of properties that may affect user experience, such as accuracy, robustness, scalability, and so forth. In this paper we discuss how to compare recommenders based on a set of properties that are relevant for the application. We focus on comparative studies, where a few algorithms are compared using some evaluation metric, rather than absolute benchmarking of algorithms. We describe experimental settings appropriate for making choices between algorithms. We review three types of experiments, starting with an offline setting, where recommendation approaches are compared without user interaction, then reviewing user studies, where a small group of subjects experiment with the system and report on the experience, and finally describe large scale online experiments, where real user populations interact with the system. In each of these cases we describe types of questions that can be answered, and suggest protocols for experimentation. We also discuss how to draw trustworthy conclusions from the conducted experiments. We then review a large set of properties, and explain how to evaluate systems given relevant properties. We also survey a large set of evaluation metrics in the context of the properties that they evaluate.

Recommendation System for Adaptive Learning.

PubMed

Chen, Yunxiao; Li, Xiaoou; Liu, Jingchen; Ying, Zhiliang

2018-01-01

An adaptive learning system aims at providing instruction tailored to the current status of a learner, differing from the traditional classroom experience. The latest advances in technology make adaptive learning possible, which has the potential to provide students with high-quality learning benefit at a low cost. A key component of an adaptive learning system is a recommendation system, which recommends the next material (video lectures, practices, and so on, on different skills) to the learner, based on the psychometric assessment results and possibly other individual characteristics. An important question then follows: How should recommendations be made? To answer this question, a mathematical framework is proposed that characterizes the recommendation process as a Markov decision problem, for which decisions are made based on the current knowledge of the learner and that of the learning materials. In particular, two plain vanilla systems are introduced, for which the optimal recommendation at each stage can be obtained analytically.

Product Recommendation System Based on Personal Preference Model Using CAM

NASA Astrophysics Data System (ADS)

Murakami, Tomoko; Yoshioka, Nobukazu; Orihara, Ryohei; Furukawa, Koichi

Product recommendation system is realized by applying business rules acquired by data maining techniques. Business rules such as demographical patterns of purchase, are able to cover the groups of users that have a tendency to purchase products, but it is difficult to recommend products adaptive to various personal preferences only by utilizing them. In addition to that, it is very costly to gather the large volume of high quality survey data, which is necessary for good recommendation based on personal preference model. A method collecting kansei information automatically without questionnaire survey is required. The constructing personal preference model from less favor data is also necessary, since it is costly for the user to input favor data. In this paper, we propose product recommendation system based on kansei information extracted by text mining and user's preference model constructed by Category-guided Adaptive Modeling, CAM for short. CAM is a feature construction method that can generate new features constructing the space where same labeled examples are close and different labeled examples are far away from some labeled examples. It is possible to construct personal preference model by CAM despite less information of likes and dislikes categories. In the system, retrieval agent gathers the products' specification and user agent manages preference model, user's likes and dislikes. Kansei information of the products is gained by applying text mining technique to the reputation documents about the products on the web site. We carry out some experimental studies to make sure that prefrence model obtained by our method performs effectively.

The Effect of Incorporating Good Learners' Ratings in e-Learning Content-Based Recommender System

ERIC Educational Resources Information Center

Ghauth, Khairil Imran; Abdullah, Nor Aniza

2011-01-01

One of the anticipated challenges of today's e-learning is to solve the problem of recommending from a large number of learning materials. In this study, we introduce a novel architecture for an e-learning recommender system. More specifically, this paper comprises the following phases i) to propose an e-learning recommender system based on…

a Context-Aware Tourism Recommender System Based on a Spreading Activation Method

NASA Astrophysics Data System (ADS)

Bahramian, Z.; Abbaspour, R. Ali; Claramunt, C.

2017-09-01

Users planning a trip to a given destination often search for the most appropriate points of interest location, this being a non-straightforward task as the range of information available is very large and not very well structured. The research presented by this paper introduces a context-aware tourism recommender system that overcomes the information overload problem by providing personalized recommendations based on the user's preferences. It also incorporates contextual information to improve the recommendation process. As previous context-aware tourism recommender systems suffer from a lack of formal definition to represent contextual information and user's preferences, the proposed system is enhanced using an ontology approach. We also apply a spreading activation technique to contextualize user preferences and learn the user profile dynamically according to the user's feedback. The proposed method assigns more effect in the spreading process for nodes which their preference values are assigned directly by the user. The results show the overall performance of the proposed context-aware tourism recommender systems by an experimental application to the city of Tehran.

Compositional descriptor-based recommender system for the materials discovery

NASA Astrophysics Data System (ADS)

Seko, Atsuto; Hayashi, Hiroyuki; Tanaka, Isao

2018-06-01

Structures and properties of many inorganic compounds have been collected historically. However, it only covers a very small portion of possible inorganic crystals, which implies the presence of numerous currently unknown compounds. A powerful machine-learning strategy is mandatory to discover new inorganic compounds from all chemical combinations. Herein we propose a descriptor-based recommender-system approach to estimate the relevance of chemical compositions where crystals can be formed [i.e., chemically relevant compositions (CRCs)]. In addition to data-driven compositional similarity used in the literature, the use of compositional descriptors as a prior knowledge is helpful for the discovery of new compounds. We validate our recommender systems in two ways. First, one database is used to construct a model, while another is used for the validation. Second, we estimate the phase stability for compounds at expected CRCs using density functional theory calculations.

A novel video recommendation system based on efficient retrieval of human actions

NASA Astrophysics Data System (ADS)

Ramezani, Mohsen; Yaghmaee, Farzin

2016-09-01

In recent years, fast growth of online video sharing eventuated new issues such as helping users to find their requirements in an efficient way. Hence, Recommender Systems (RSs) are used to find the users' most favorite items. Finding these items relies on items or users similarities. Though, many factors like sparsity and cold start user impress the recommendation quality. In some systems, attached tags are used for searching items (e.g. videos) as personalized recommendation. Different views, incomplete and inaccurate tags etc. can weaken the performance of these systems. Considering the advancement of computer vision techniques can help improving RSs. To this end, content based search can be used for finding items (here, videos are considered). In such systems, a video is taken from the user to find and recommend a list of most similar videos to the query one. Due to relating most videos to humans, we present a novel low complex scalable method to recommend videos based on the model of included action. This method has recourse to human action retrieval approaches. For modeling human actions, some interest points are extracted from each action and their motion information are used to compute the action representation. Moreover, a fuzzy dissimilarity measure is presented to compare videos for ranking them. The experimental results on HMDB, UCFYT, UCF sport and KTH datasets illustrated that, in most cases, the proposed method can reach better results than most used methods.

Network-based recommendation algorithms: A review

NASA Astrophysics Data System (ADS)

Yu, Fei; Zeng, An; Gillard, Sébastien; Medo, Matúš

2016-06-01

Recommender systems are a vital tool that helps us to overcome the information overload problem. They are being used by most e-commerce web sites and attract the interest of a broad scientific community. A recommender system uses data on users' past preferences to choose new items that might be appreciated by a given individual user. While many approaches to recommendation exist, the approach based on a network representation of the input data has gained considerable attention in the past. We review here a broad range of network-based recommendation algorithms and for the first time compare their performance on three distinct real datasets. We present recommendation topics that go beyond the mere question of which algorithm to use-such as the possible influence of recommendation on the evolution of systems that use it-and finally discuss open research directions and challenges.

E-book recommender system design and implementation based on data mining

NASA Astrophysics Data System (ADS)

Wang, Zongjiang

2011-12-01

In the knowledge explosion, rapid development of information age, how quickly the user or users interested in useful information for feedback to the user problem to be solved in this article. This paper based on data mining, association rules to the model and classification model a combination of electronic books on the recommendation of the user's neighboring users interested in e-books to target users. Introduced the e-book recommendation and the key technologies, system implementation algorithms, and implementation process, was proved through experiments that this system can help users quickly find the required e-books.

Matrix- and tensor-based recommender systems for the discovery of currently unknown inorganic compounds

NASA Astrophysics Data System (ADS)

Seko, Atsuto; Hayashi, Hiroyuki; Kashima, Hisashi; Tanaka, Isao

2018-01-01

Chemically relevant compositions (CRCs) and atomic arrangements of inorganic compounds have been collected as inorganic crystal structure databases. Machine learning is a unique approach to search for currently unknown CRCs from vast candidates. Herein we propose matrix- and tensor-based recommender system approaches to predict currently unknown CRCs from database entries of CRCs. Firstly, the performance of the recommender system approaches to discover currently unknown CRCs is examined. A Tucker decomposition recommender system shows the best discovery rate of CRCs as the majority of the top 100 recommended ternary and quaternary compositions correspond to CRCs. Secondly, systematic density functional theory (DFT) calculations are performed to investigate the phase stability of the recommended compositions. The phase stability of the 27 compositions reveals that 23 currently unknown compounds are newly found to be stable. These results indicate that the recommender system has great potential to accelerate the discovery of new compounds.

Aggregation Trade Offs in Family Based Recommendations

NASA Astrophysics Data System (ADS)

Berkovsky, Shlomo; Freyne, Jill; Coombe, Mac

Personalized information access tools are frequently based on collaborative filtering recommendation algorithms. Collaborative filtering recommender systems typically suffer from a data sparsity problem, where systems do not have sufficient user data to generate accurate and reliable predictions. Prior research suggested using group-based user data in the collaborative filtering recommendation process to generate group-based predictions and partially resolve the sparsity problem. Although group recommendations are less accurate than personalized recommendations, they are more accurate than general non-personalized recommendations, which are the natural fall back when personalized recommendations cannot be generated. In this work we present initial results of a study that exploits the browsing logs of real families of users gathered in an eHealth portal. The browsing logs allowed us to experimentally compare the accuracy of two group-based recommendation strategies: aggregated group models and aggregated predictions. Our results showed that aggregating individual models into group models resulted in more accurate predictions than aggregating individual predictions into group predictions.

HealthRecSys: A semantic content-based recommender system to complement health videos.

PubMed

Sanchez Bocanegra, Carlos Luis; Sevillano Ramos, Jose Luis; Rizo, Carlos; Civit, Anton; Fernandez-Luque, Luis

2017-05-15

The Internet, and its popularity, continues to grow at an unprecedented pace. Watching videos online is very popular; it is estimated that 500 h of video are uploaded onto YouTube, a video-sharing service, every minute and that, by 2019, video formats will comprise more than 80% of Internet traffic. Health-related videos are very popular on YouTube, but their quality is always a matter of concern. One approach to enhancing the quality of online videos is to provide additional educational health content, such as websites, to support health consumers. This study investigates the feasibility of building a content-based recommender system that links health consumers to reputable health educational websites from MedlinePlus for a given health video from YouTube. The dataset for this study includes a collection of health-related videos and their available metadata. Semantic technologies (such as SNOMED-CT and Bio-ontology) were used to recommend health websites from MedlinePlus. A total of 26 healths professionals participated in evaluating 253 recommended links for a total of 53 videos about general health, hypertension, or diabetes. The relevance of the recommended health websites from MedlinePlus to the videos was measured using information retrieval metrics such as the normalized discounted cumulative gain and precision at K. The majority of websites recommended by our system for health videos were relevant, based on ratings by health professionals. The normalized discounted cumulative gain was between 46% and 90% for the different topics. Our study demonstrates the feasibility of using a semantic content-based recommender system to enrich YouTube health videos. Evaluation with end-users, in addition to healthcare professionals, will be required to identify the acceptance of these recommendations in a nonsimulated information-seeking context.

Recommender systems

NASA Astrophysics Data System (ADS)

Lü, Linyuan; Medo, Matúš; Yeung, Chi Ho; Zhang, Yi-Cheng; Zhang, Zi-Ke; Zhou, Tao

2012-10-01

The ongoing rapid expansion of the Internet greatly increases the necessity of effective recommender systems for filtering the abundant information. Extensive research for recommender systems is conducted by a broad range of communities including social and computer scientists, physicists, and interdisciplinary researchers. Despite substantial theoretical and practical achievements, unification and comparison of different approaches are lacking, which impedes further advances. In this article, we review recent developments in recommender systems and discuss the major challenges. We compare and evaluate available algorithms and examine their roles in the future developments. In addition to algorithms, physical aspects are described to illustrate macroscopic behavior of recommender systems. Potential impacts and future directions are discussed. We emphasize that recommendation has great scientific depth and combines diverse research fields which makes it interesting for physicists as well as interdisciplinary researchers.

N-Screen Aware Multicriteria Hybrid Recommender System Using Weight Based Subspace Clustering

PubMed Central

Ullah, Farman; Lee, Sungchang

2014-01-01

This paper presents a recommender system for N-screen services in which users have multiple devices with different capabilities. In N-screen services, a user can use various devices in different locations and time and can change a device while the service is running. N-screen aware recommendation seeks to improve the user experience with recommended content by considering the user N-screen device attributes such as screen resolution, media codec, remaining battery time, and access network and the user temporal usage pattern information that are not considered in existing recommender systems. For N-screen aware recommendation support, this work introduces a user device profile collaboration agent, manager, and N-screen control server to acquire and manage the user N-screen devices profile. Furthermore, a multicriteria hybrid framework is suggested that incorporates the N-screen devices information with user preferences and demographics. In addition, we propose an individual feature and subspace weight based clustering (IFSWC) to assign different weights to each subspace and each feature within a subspace in the hybrid framework. The proposed system improves the accuracy, precision, scalability, sparsity, and cold start issues. The simulation results demonstrate the effectiveness and prove the aforementioned statements. PMID:25152921

Shilling Attacks Detection in Recommender Systems Based on Target Item Analysis

PubMed Central

Zhou, Wei; Wen, Junhao; Koh, Yun Sing; Xiong, Qingyu; Gao, Min; Dobbie, Gillian; Alam, Shafiq

2015-01-01

Recommender systems are highly vulnerable to shilling attacks, both by individuals and groups. Attackers who introduce biased ratings in order to affect recommendations, have been shown to negatively affect collaborative filtering (CF) algorithms. Previous research focuses only on the differences between genuine profiles and attack profiles, ignoring the group characteristics in attack profiles. In this paper, we study the use of statistical metrics to detect rating patterns of attackers and group characteristics in attack profiles. Another question is that most existing detecting methods are model specific. Two metrics, Rating Deviation from Mean Agreement (RDMA) and Degree of Similarity with Top Neighbors (DegSim), are used for analyzing rating patterns between malicious profiles and genuine profiles in attack models. Building upon this, we also propose and evaluate a detection structure called RD-TIA for detecting shilling attacks in recommender systems using a statistical approach. In order to detect more complicated attack models, we propose a novel metric called DegSim’ based on DegSim. The experimental results show that our detection model based on target item analysis is an effective approach for detecting shilling attacks. PMID:26222882

Axillary lymph node uptake of technetium-99m-MDP

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ongseng, F.; Goldfarb, C.R.; Finestone, H.

We sought to determine the frequency and significance of axillary lymph node visualization on bone scans performed with diphosphonates. Consecutive {sup 99m}Tc-methylene diphosphonate ({sup 99m}Tc-MDP) bone scans (2435) were inspected for axillary soft-tissue uptake. In positive cases, the results of physical examination, correlative imaging studies and serial bone scans were recorded, as was the site of venipuncture. Forty-eight studies (2%) showed axillary uptake ipsilateral to the injection site. Extravasation of tracer, documented by focal activity near the injection site, was present in every case. There was no association with axillary adenopathy, mass, induration of radiographically visible calcification. On some images,more » foci adjacent to the axilla were superimposed on the rib, scapula, or humerus. The bone-to-background ratio was frequently reduced; repeat imaging after 1-2 hr usually improved osseous detail. Ipsilateral axillary lymph node visualization due to extravasation of {sup 99m}Tc-MDP is frequently associated with additional foci superimposed on osseous structures simulating pathology. Delayed skeletal uptake is common in such cases and necessitates a greater time interval between injection and imaging. 7 refs., 3 figs.« less

Promoting Cold-Start Items in Recommender Systems

PubMed Central

Liu, Jin-Hu; Zhou, Tao; Zhang, Zi-Ke; Yang, Zimo; Liu, Chuang; Li, Wei-Min

2014-01-01

As one of the major challenges, cold-start problem plagues nearly all recommender systems. In particular, new items will be overlooked, impeding the development of new products online. Given limited resources, how to utilize the knowledge of recommender systems and design efficient marketing strategy for new items is extremely important. In this paper, we convert this ticklish issue into a clear mathematical problem based on a bipartite network representation. Under the most widely used algorithm in real e-commerce recommender systems, the so-called item-based collaborative filtering, we show that to simply push new items to active users is not a good strategy. Interestingly, experiments on real recommender systems indicate that to connect new items with some less active users will statistically yield better performance, namely, these new items will have more chance to appear in other users' recommendation lists. Further analysis suggests that the disassortative nature of recommender systems contributes to such observation. In a word, getting in-depth understanding on recommender systems could pave the way for the owners to popularize their cold-start products with low costs. PMID:25479013

Promoting cold-start items in recommender systems.

PubMed

Liu, Jin-Hu; Zhou, Tao; Zhang, Zi-Ke; Yang, Zimo; Liu, Chuang; Li, Wei-Min

2014-01-01

As one of the major challenges, cold-start problem plagues nearly all recommender systems. In particular, new items will be overlooked, impeding the development of new products online. Given limited resources, how to utilize the knowledge of recommender systems and design efficient marketing strategy for new items is extremely important. In this paper, we convert this ticklish issue into a clear mathematical problem based on a bipartite network representation. Under the most widely used algorithm in real e-commerce recommender systems, the so-called item-based collaborative filtering, we show that to simply push new items to active users is not a good strategy. Interestingly, experiments on real recommender systems indicate that to connect new items with some less active users will statistically yield better performance, namely, these new items will have more chance to appear in other users' recommendation lists. Further analysis suggests that the disassortative nature of recommender systems contributes to such observation. In a word, getting in-depth understanding on recommender systems could pave the way for the owners to popularize their cold-start products with low costs.

Weighted hybrid technique for recommender system

NASA Astrophysics Data System (ADS)

Suriati, S.; Dwiastuti, Meisyarah; Tulus, T.

2017-12-01

Recommender system becomes very popular and has important role in an information system or webpages nowadays. A recommender system tries to make a prediction of which item a user may like based on his activity on the system. There are some familiar techniques to build a recommender system, such as content-based filtering and collaborative filtering. Content-based filtering does not involve opinions from human to make the prediction, while collaborative filtering does, so collaborative filtering can predict more accurately. However, collaborative filtering cannot give prediction to items which have never been rated by any user. In order to cover the drawbacks of each approach with the advantages of other approach, both approaches can be combined with an approach known as hybrid technique. Hybrid technique used in this work is weighted technique in which the prediction score is combination linear of scores gained by techniques that are combined.The purpose of this work is to show how an approach of weighted hybrid technique combining content-based filtering and item-based collaborative filtering can work in a movie recommender system and to show the performance comparison when both approachare combined and when each approach works alone. There are three experiments done in this work, combining both techniques with different parameters. The result shows that the weighted hybrid technique that is done in this work does not really boost the performance up, but it helps to give prediction score for unrated movies that are impossible to be recommended by only using collaborative filtering.

Personalized Recommender System for Digital Libraries

ERIC Educational Resources Information Center

Omisore, M. O.; Samuel, O. W.

2014-01-01

The huge amount of information available online has given rise to personalization and filtering systems. Recommender systems (RS) constitute a specific type of information filtering technique that present items according to user's interests. In this research, a web-based personalized recommender system capable of providing learners with books that…

A Collaborative Recommend Algorithm Based on Bipartite Community

PubMed Central

Fu, Yuchen; Liu, Quan; Cui, Zhiming

2014-01-01

The recommendation algorithm based on bipartite network is superior to traditional methods on accuracy and diversity, which proves that considering the network topology of recommendation systems could help us to improve recommendation results. However, existing algorithms mainly focus on the overall topology structure and those local characteristics could also play an important role in collaborative recommend processing. Therefore, on account of data characteristics and application requirements of collaborative recommend systems, we proposed a link community partitioning algorithm based on the label propagation and a collaborative recommendation algorithm based on the bipartite community. Then we designed numerical experiments to verify the algorithm validity under benchmark and real database. PMID:24955393

Technetium-99 conjugated with methylene diphosphonate (99Tc-MDP) inhibits experimental choroidal neovascularization in vivo and VEGF-induced cell migration and tube formation in vitro.

PubMed

Lai, Kunbei; Xu, Li; Jin, Chenjin; Wu, Kaili; Tian, Zhen; Huang, Chuangxin; Zhong, Xiaojing; Ye, Haiyun

2011-07-29

To investigate the effects of (99)Tc-MDP, a decay product of (99m)Tc-MDP, on the development of choroidal neovascularization (CNV), together with its underlying mechanisms. C57BL/6J mice were used to induce CNV by laser photocoagulation. (99)Tc-MDP at the doses of 0.5 × 10(-1), 1 × 10(-1), and 2 × 10(-1) μg/kg or the same volume of PBS was intraperitoneally injected daily after photocoagulation until the end of the experiment. Seven days after laser injury, mice were perfused with fluorescein-labeled dextran, and areas of CNV were measured. Numbers of infiltrating macrophages, protein levels of VEGF, and inflammation-related molecules including intercellular adhesion molecule (ICAM)-1, tumor necrosis factor (TNF)-α, and matrix metalloproteinases (MMPs) in the RPE-choroid complex were detected 3 days after laser photocoagulation. Effects of (99)Tc-MDP on VEGF-induced endothelial cell migration and tube formation were also studied. Toxicity of (99)Tc-MDP was evaluated in vivo and in vitro. Areas of CNV were significantly suppressed by (99)Tc-MDP treatment without toxicity to the retina compared with PBS treatment in a dose-dependent manner: (99)Tc-MDP treatment of 0.5 × 10(-1) μg/kg (5698.60 ± 1037.70 μm(2)), 1 × 10(-1) μg/kg (3678.34 ± 1328.18 μm(2)), and 2 × 10(-1) μg/kg (2365.78 ± 923.80 μm(2)) suppressed the development of CNV by 36.12%, 58.76%, and 73.48%, respectively, compared with that in the PBS treatment group (8920.36 ± 1097.29 μm(2); P < 0.001). (99)Tc-MDP treatment led to significant inhibition of macrophages infiltrating to CNV together with downregulated protein expressions of VEGF, ICAM-1, TNF-α, and MMP-2. (99)Tc-MDP also showed an inhibitive effect on cell proliferation and VEGF-induced migration and capillary-like tube formation of endothelial cells. Anti-inflammatory treatment with (99)Tc-MDP has therapeutic potential for CNV-related diseases.

Mobile Clinical Decision Support System for Acid-base Balance Diagnosis and Treatment Recommendation.

PubMed

Mandzuka, Mensur; Begic, Edin; Boskovic, Dusanka; Begic, Zijo; Masic, Izet

2017-06-01

This paper presents mobile application implementing a decision support system for acid-base disorder diagnosis and treatment recommendation. The application was developed using the official integrated development environment for the Android platform (to maximize availability and minimize investments in specialized hardware) called Android Studio. The application identifies disorder, based on the blood gas analysis, evaluates whether the disorder has been compensated, and based on additional input related to electrolyte imbalance, provides recommendations for treatment. The application is a tool in the hands of the user, which provides assistance during acid-base disorders treatment. The application will assist the physician in clinical practice and is focused on the treatment in intensive care.

Mobile Clinical Decision Support System for Acid-base Balance Diagnosis and Treatment Recommendation

PubMed Central

Mandzuka, Mensur; Begic, Edin; Boskovic, Dusanka; Begic, Zijo; Masic, Izet

2017-01-01

Introduction: This paper presents mobile application implementing a decision support system for acid-base disorder diagnosis and treatment recommendation. Material and methods: The application was developed using the official integrated development environment for the Android platform (to maximize availability and minimize investments in specialized hardware) called Android Studio. Results: The application identifies disorder, based on the blood gas analysis, evaluates whether the disorder has been compensated, and based on additional input related to electrolyte imbalance, provides recommendations for treatment. Conclusion: The application is a tool in the hands of the user, which provides assistance during acid-base disorders treatment. The application will assist the physician in clinical practice and is focused on the treatment in intensive care. PMID:28883678

A Geospatial Data Recommender System based on Metadata and User Behaviour

NASA Astrophysics Data System (ADS)

Li, Y.; Jiang, Y.; Yang, C. P.; Armstrong, E. M.; Huang, T.; Moroni, D. F.; Finch, C. J.; McGibbney, L. J.

2017-12-01

Earth observations are produced in a fast velocity through real time sensors, reaching tera- to peta- bytes of geospatial data daily. Discovering and accessing the right data from the massive geospatial data is like finding needle in the haystack. To help researchers find the right data for study and decision support, quite a lot of research focusing on improving search performance have been proposed including recommendation algorithm. However, few papers have discussed the way to implement a recommendation algorithm in geospatial data retrieval system. In order to address this problem, we propose a recommendation engine to improve discovering relevant geospatial data by mining and utilizing metadata and user behavior data: 1) metadata based recommendation considers the correlation of each attribute (i.e., spatiotemporal, categorical, and ordinal) to data to be found. In particular, phrase extraction method is used to improve the accuracy of the description similarity; 2) user behavior data are utilized to predict the interest of a user through collaborative filtering; 3) an integration method is designed to combine the results of the above two methods to achieve better recommendation Experiments show that in the hybrid recommendation list, the all the precisions are larger than 0.8 from position 1 to 10.

Measuring Learner's Performance in E-Learning Recommender Systems

ERIC Educational Resources Information Center

Ghauth, Khairil Imran; Abdullah, Nor Aniza

2010-01-01

A recommender system is a piece of software that helps users to identify the most interesting and relevant learning items from a large number of items. Recommender systems may be based on collaborative filtering (by user ratings), content-based filtering (by keywords), and hybrid filtering (by both collaborative and content-based filtering).…

Shilling attack detection for recommender systems based on credibility of group users and rating time series.

PubMed

Zhou, Wei; Wen, Junhao; Qu, Qiang; Zeng, Jun; Cheng, Tian

2018-01-01

Recommender systems are vulnerable to shilling attacks. Forged user-generated content data, such as user ratings and reviews, are used by attackers to manipulate recommendation rankings. Shilling attack detection in recommender systems is of great significance to maintain the fairness and sustainability of recommender systems. The current studies have problems in terms of the poor universality of algorithms, difficulty in selection of user profile attributes, and lack of an optimization mechanism. In this paper, a shilling behaviour detection structure based on abnormal group user findings and rating time series analysis is proposed. This paper adds to the current understanding in the field by studying the credibility evaluation model in-depth based on the rating prediction model to derive proximity-based predictions. A method for detecting suspicious ratings based on suspicious time windows and target item analysis is proposed. Suspicious rating time segments are determined by constructing a time series, and data streams of the rating items are examined and suspicious rating segments are checked. To analyse features of shilling attacks by a group user's credibility, an abnormal group user discovery method based on time series and time window is proposed. Standard testing datasets are used to verify the effect of the proposed method.

Shilling attack detection for recommender systems based on credibility of group users and rating time series

PubMed Central

Wen, Junhao; Qu, Qiang; Zeng, Jun; Cheng, Tian

2018-01-01

Recommender systems are vulnerable to shilling attacks. Forged user-generated content data, such as user ratings and reviews, are used by attackers to manipulate recommendation rankings. Shilling attack detection in recommender systems is of great significance to maintain the fairness and sustainability of recommender systems. The current studies have problems in terms of the poor universality of algorithms, difficulty in selection of user profile attributes, and lack of an optimization mechanism. In this paper, a shilling behaviour detection structure based on abnormal group user findings and rating time series analysis is proposed. This paper adds to the current understanding in the field by studying the credibility evaluation model in-depth based on the rating prediction model to derive proximity-based predictions. A method for detecting suspicious ratings based on suspicious time windows and target item analysis is proposed. Suspicious rating time segments are determined by constructing a time series, and data streams of the rating items are examined and suspicious rating segments are checked. To analyse features of shilling attacks by a group user’s credibility, an abnormal group user discovery method based on time series and time window is proposed. Standard testing datasets are used to verify the effect of the proposed method. PMID:29742134

Space shuttle recommendations based on aircraft maintenance experience

NASA Technical Reports Server (NTRS)

Spears, J. M.; Fox, C. L.

1972-01-01

Space shuttle design recommendations based on aircraft maintenance experience are developed. The recommendations are specifically applied to the landing gear system, nondestructive inspection techniques, hydraulic system design, materials and processes, and program support.

Recommendation advertising method based on behavior retargeting

NASA Astrophysics Data System (ADS)

Zhao, Yao; YIN, Xin-Chun; CHEN, Zhi-Min

2011-10-01

Online advertising has become an important business in e-commerce. Ad recommended algorithms are the most critical part in recommendation systems. We propose a recommendation advertising method based on behavior retargeting which can avoid leakage click of advertising due to objective reasons and can observe the changes of the user's interest in time. Experiments show that our new method can have a significant effect and can be further to apply to online system.

Science Concierge: A Fast Content-Based Recommendation System for Scientific Publications.

PubMed

Achakulvisut, Titipat; Acuna, Daniel E; Ruangrong, Tulakan; Kording, Konrad

2016-01-01

Finding relevant publications is important for scientists who have to cope with exponentially increasing numbers of scholarly material. Algorithms can help with this task as they help for music, movie, and product recommendations. However, we know little about the performance of these algorithms with scholarly material. Here, we develop an algorithm, and an accompanying Python library, that implements a recommendation system based on the content of articles. Design principles are to adapt to new content, provide near-real time suggestions, and be open source. We tested the library on 15K posters from the Society of Neuroscience Conference 2015. Human curated topics are used to cross validate parameters in the algorithm and produce a similarity metric that maximally correlates with human judgments. We show that our algorithm significantly outperformed suggestions based on keywords. The work presented here promises to make the exploration of scholarly material faster and more accurate.

Science Concierge: A Fast Content-Based Recommendation System for Scientific Publications

PubMed Central

Achakulvisut, Titipat; Acuna, Daniel E.; Ruangrong, Tulakan; Kording, Konrad

2016-01-01

Finding relevant publications is important for scientists who have to cope with exponentially increasing numbers of scholarly material. Algorithms can help with this task as they help for music, movie, and product recommendations. However, we know little about the performance of these algorithms with scholarly material. Here, we develop an algorithm, and an accompanying Python library, that implements a recommendation system based on the content of articles. Design principles are to adapt to new content, provide near-real time suggestions, and be open source. We tested the library on 15K posters from the Society of Neuroscience Conference 2015. Human curated topics are used to cross validate parameters in the algorithm and produce a similarity metric that maximally correlates with human judgments. We show that our algorithm significantly outperformed suggestions based on keywords. The work presented here promises to make the exploration of scholarly material faster and more accurate. PMID:27383424

Evidence-based management of systemic sclerosis: Navigating recommendations and guidelines.

PubMed

Pellar, Russell Edward; Pope, Janet Elizabeth

2017-06-01

Systemic sclerosis (SSc) is a rare heterogeneous connective tissue disease. Recommendations addressing the major issues in the management of SSc including screening and treatment of organ complications are needed. The updated European League Against Rheumatism/European Scleroderma Trial and Research (EULAR/EUSTAR) and the British Society of Rheumatology (BSR) and British Health Professionals in Rheumatology (BHPR) guidelines were compared and contrasted. The updated EULAR/EUSTAR guidelines focus specifically on the management of SSc features and include data on newer therapeutic modalities and mention a research agenda. These recommendations are pharmacologic, with few guidelines regarding investigations and non-pharmacologic management. Recommendations from BSR/BHPR are similar to the organ manifestations mentioned in the EULAR/EUSTAR recommendations, and expand on several domains of treatment, including general measures, non-pharmacologic treatment, cardiac involvement, calcinosis, and musculoskeletal features. The guidelines usually agree with one another. Limitations include the lack of guidance for combination or second-line therapy, algorithmic suggestions, the absence of evidence-based recommendations regarding the treatment of specific complications (i.e., gastric antral ectasia and erectile dysfunction). Consensus for when to treat interstitial lung disease in SSc is lacking. There are differences between Europe and North American experts due to access and indications for certain therapies. Care gaps in SSc have been demonstrated so the EULAR/EUSTAR and BSR/BHP guidelines can promote best practices. Certain complications warrant active investigation to further improve outcomes in SSc and future updates of these recommendations. Care gaps in SSc have been demonstrated so the EULAR/EUSTAR and BSR/BHP guidelines can promote best practices. Certain complications warrant active investigation to further improve outcomes in SSc. Copyright © 2017 Elsevier Inc

Development of a Recommender System based on Personal History

NASA Astrophysics Data System (ADS)

Tanaka, Katsuaki; Hori, Koichi; Yamamoto, Masato

The flood of information on the Internet makes a person who surf it without some strong intention strayed into it. One of the ways to control the balance between a person and the flood is a recommender system by computer, and many web sites use it. These systems work on a web site for the same kind of items. However the field of personal activity is not limited to handle the same kind of thing and a web site, but also offline area in the real world. To handle personal offline activities, LifeLog is proposed as method to record it, but the main purpose of LifeLog is recording a personal history. How to use a history has still been studied stage. The authors have developed a recommender system that captures personal context from history of personal online and offline activities, treats information on web sites as a large set of context, and finds out and extends overlap of them, then recommends information located there. The aim of the system is that a person can enjoy waves of information again. The system worked as a part of My-life Assist Service. It was a service for mobile phones provided by NTT DoCoMo, Inc. as a field experiment from Dec. 2007 to Feb. 2008.

Effects and safety of 99Tc-MDP in patients with refractory ankylosing spondylitis: a 2-stage (30-week follow-up) clinical trial.

PubMed

Xu, Yunyun; Zhong, Yi; Zhao, Minjing; Tu, Liudan; Fan, Meida; Zhang, Pingping; Wei, Qiujing; Cao, Shuangyan; Li, Qiuxia; Liao, Zetao; Lin, Zhiming; Pan, Yunfeng; Jin, Ou; Gu, Jieruo

2018-01-01

To evaluate the clinical efficacy and safety in patients with refractory ankylosing spondylitis (AS) initiating 99Tc-MDP therapy and explore the mechanisms. Refractory AS patients were enrolled in the clinical trial and received 99Tc-MDP treatments for 3 or 5 courses according to ASAS improvement. Efficacy and safety evaluations were conducted during the follow-up. 37 cytokines were quantified by Luminex at baseline and week 30. p-values<0.05 were considered statistically significant. 51 refractory AS patients were included, with 20 healthy people serving as the control group. The patients were in an active disease state (mean (SD) ASDAS 3.66 (0.83), BASDAI 4.53 (1.92)), 42(82.35%) patients had syndesmophytes. Their cytokines were significantly higher than that in the control group. After 3 courses of 99Tc-MDP treatment, 32 (62.75%) patients achieved ASAS20 improvement, 24 (47.06%) patients achieved a clinically significant improvement (ΔASDAS-CRP≥1.1). 27 patients entered the second stage to complete 5 courses of the treatment, all of whom achieved ASAS20 improvement, 18 (66.67%) patients achieved a clinically significant improvement. All clinical parameters including ASAS and ASDAS significantly improved as the treatment was continued. Cytokines also had significant down-regulation after the treatment, and the reductions had positive correlations with the improvements of disease activity. No serious adverse event was observed. This investigation confirmed the remarkable efficacy of 99Tc-MDP in a large number of refractory AS patients, and highlighted the mechanism by dramatic regulation on cytokines. 99Tc-MDP was safe in clinical application.

Research on personalized recommendation algorithm based on spark

NASA Astrophysics Data System (ADS)

Li, Zeng; Liu, Yu

2018-04-01

With the increasing amount of data in the past years, the traditional recommendation algorithm has been unable to meet people's needs. Therefore, how to better recommend their products to users of interest, become the opportunities and challenges of the era of big data development. At present, each platform enterprise has its own recommendation algorithm, but how to make efficient and accurate push information is still an urgent problem for personalized recommendation system. In this paper, a hybrid algorithm based on user collaborative filtering and content-based recommendation algorithm is proposed on Spark to improve the efficiency and accuracy of recommendation by weighted processing. The experiment shows that the recommendation under this scheme is more efficient and accurate.

CROSS-DISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY: Diffusion-Based Recommendation in Collaborative Tagging Systems

NASA Astrophysics Data System (ADS)

Shang, Ming-Sheng; Zhang, Zi-Ke

2009-11-01

Recently, collaborative tagging systems have attracted more and more attention and have been widely applied in web systems. Tags provide highly abstracted information about personal preferences and item content, and therefore have the potential to help in improving better personalized recommendations. We propose a diffusion-based recommendation algorithm considering the personal vocabulary and evaluate it in a real-world dataset: Del.icio.us. Experimental results demonstrate that the usage of tag information can significantly improve the accuracy of personalized recommendations.

Item Anomaly Detection Based on Dynamic Partition for Time Series in Recommender Systems

PubMed Central

Gao, Min; Tian, Renli; Wen, Junhao; Xiong, Qingyu; Ling, Bin; Yang, Linda

2015-01-01

In recent years, recommender systems have become an effective method to process information overload. However, recommendation technology still suffers from many problems. One of the problems is shilling attacks-attackers inject spam user profiles to disturb the list of recommendation items. There are two characteristics of all types of shilling attacks: 1) Item abnormality: The rating of target items is always maximum or minimum; and 2) Attack promptness: It takes only a very short period time to inject attack profiles. Some papers have proposed item anomaly detection methods based on these two characteristics, but their detection rate, false alarm rate, and universality need to be further improved. To solve these problems, this paper proposes an item anomaly detection method based on dynamic partitioning for time series. This method first dynamically partitions item-rating time series based on important points. Then, we use chi square distribution (χ2) to detect abnormal intervals. The experimental results on MovieLens 100K and 1M indicate that this approach has a high detection rate and a low false alarm rate and is stable toward different attack models and filler sizes. PMID:26267477

Item Anomaly Detection Based on Dynamic Partition for Time Series in Recommender Systems.

PubMed

Gao, Min; Tian, Renli; Wen, Junhao; Xiong, Qingyu; Ling, Bin; Yang, Linda

2015-01-01

In recent years, recommender systems have become an effective method to process information overload. However, recommendation technology still suffers from many problems. One of the problems is shilling attacks-attackers inject spam user profiles to disturb the list of recommendation items. There are two characteristics of all types of shilling attacks: 1) Item abnormality: The rating of target items is always maximum or minimum; and 2) Attack promptness: It takes only a very short period time to inject attack profiles. Some papers have proposed item anomaly detection methods based on these two characteristics, but their detection rate, false alarm rate, and universality need to be further improved. To solve these problems, this paper proposes an item anomaly detection method based on dynamic partitioning for time series. This method first dynamically partitions item-rating time series based on important points. Then, we use chi square distribution (χ2) to detect abnormal intervals. The experimental results on MovieLens 100K and 1M indicate that this approach has a high detection rate and a low false alarm rate and is stable toward different attack models and filler sizes.

A Group Recommender System for Tourist Activities

NASA Astrophysics Data System (ADS)

Garcia, Inma; Sebastia, Laura; Onaindia, Eva; Guzman, Cesar

This paper introduces a method for giving recommendations of tourist activities to a group of users. This method makes recommendations based on the group tastes, their demographic classification and the places visited by the users in former trips. The group recommendation is computed from individual personal recommendations through the use of techniques such as aggregation, intersection or incremental intersection. This method is implemented as an extension of the e-Tourism tool, which is a user-adapted tourism and leisure application, whose main component is the Generalist Recommender System Kernel (GRSK), a domain-independent taxonomy-driven search engine that manages the group recommendation.

Solving the stability-accuracy-diversity dilemma of recommender systems

NASA Astrophysics Data System (ADS)

Hou, Lei; Liu, Kecheng; Liu, Jianguo; Zhang, Runtong

2017-02-01

Recommender systems are of great significance in predicting the potential interesting items based on the target user's historical selections. However, the recommendation list for a specific user has been found changing vastly when the system changes, due to the unstable quantification of item similarities, which is defined as the recommendation stability problem. To improve the similarity stability and recommendation stability is crucial for the user experience enhancement and the better understanding of user interests. While the stability as well as accuracy of recommendation could be guaranteed by recommending only popular items, studies have been addressing the necessity of diversity which requires the system to recommend unpopular items. By ranking the similarities in terms of stability and considering only the most stable ones, we present a top- n-stability method based on the Heat Conduction algorithm (denoted as TNS-HC henceforth) for solving the stability-accuracy-diversity dilemma. Experiments on four benchmark data sets indicate that the TNS-HC algorithm could significantly improve the recommendation stability and accuracy simultaneously and still retain the high-diversity nature of the Heat Conduction algorithm. Furthermore, we compare the performance of the TNS-HC algorithm with a number of benchmark recommendation algorithms. The result suggests that the TNS-HC algorithm is more efficient in solving the stability-accuracy-diversity triple dilemma of recommender systems.

Healthcare information systems: data mining methods in the creation of a clinical recommender system

NASA Astrophysics Data System (ADS)

Duan, L.; Street, W. N.; Xu, E.

2011-05-01

Recommender systems have been extensively studied to present items, such as movies, music and books that are likely of interest to the user. Researchers have indicated that integrated medical information systems are becoming an essential part of the modern healthcare systems. Such systems have evolved to an integrated enterprise-wide system. In particular, such systems are considered as a type of enterprise information systems or ERP system addressing healthcare industry sector needs. As part of efforts, nursing care plan recommender systems can provide clinical decision support, nursing education, clinical quality control, and serve as a complement to existing practice guidelines. We propose to use correlations among nursing diagnoses, outcomes and interventions to create a recommender system for constructing nursing care plans. In the current study, we used nursing diagnosis data to develop the methodology. Our system utilises a prefix-tree structure common in itemset mining to construct a ranked list of suggested care plan items based on previously-entered items. Unlike common commercial systems, our system makes sequential recommendations based on user interaction, modifying a ranked list of suggested items at each step in care plan construction. We rank items based on traditional association-rule measures such as support and confidence, as well as a novel measure that anticipates which selections might improve the quality of future rankings. Since the multi-step nature of our recommendations presents problems for traditional evaluation measures, we also present a new evaluation method based on average ranking position and use it to test the effectiveness of different recommendation strategies.

Context-Aware Recommender Systems

NASA Astrophysics Data System (ADS)

Adomavicius, Gediminas; Tuzhilin, Alexander

The importance of contextual information has been recognized by researchers and practitioners in many disciplines, including e-commerce personalization, information retrieval, ubiquitous and mobile computing, data mining, marketing, and management. While a substantial amount of research has already been performed in the area of recommender systems, most existing approaches focus on recommending the most relevant items to users without taking into account any additional contextual information, such as time, location, or the company of other people (e.g., for watching movies or dining out). In this chapter we argue that relevant contextual information does matter in recommender systems and that it is important to take this information into account when providing recommendations. We discuss the general notion of context and how it can be modeled in recommender systems. Furthermore, we introduce three different algorithmic paradigms - contextual prefiltering, post-filtering, and modeling - for incorporating contextual information into the recommendation process, discuss the possibilities of combining several contextaware recommendation techniques into a single unifying approach, and provide a case study of one such combined approach. Finally, we present additional capabilities for context-aware recommenders and discuss important and promising directions for future research.

Coarse cluster enhancing collaborative recommendation for social network systems

NASA Astrophysics Data System (ADS)

Zhao, Yao-Dong; Cai, Shi-Min; Tang, Ming; Shang, Min-Sheng

2017-10-01

Traditional collaborative filtering based recommender systems for social network systems bring very high demands on time complexity due to computing similarities of all pairs of users via resource usages and annotation actions, which thus strongly suppresses recommending speed. In this paper, to overcome this drawback, we propose a novel approach, namely coarse cluster that partitions similar users and associated items at a high speed to enhance user-based collaborative filtering, and then develop a fast collaborative user model for the social tagging systems. The experimental results based on Delicious dataset show that the proposed model is able to dramatically reduce the processing time cost greater than 90 % and relatively improve the accuracy in comparison with the ordinary user-based collaborative filtering, and is robust for the initial parameter. Most importantly, the proposed model can be conveniently extended by introducing more users' information (e.g., profiles) and practically applied for the large-scale social network systems to enhance the recommending speed without accuracy loss.

Recommender system based on scarce information mining.

PubMed

Lu, Wei; Chung, Fu-Lai; Lai, Kunfeng; Zhang, Liang

2017-09-01

Guessing what user may like is now a typical interface for video recommendation. Nowadays, the highly popular user generated content sites provide various sources of information such as tags for recommendation tasks. Motivated by a real world online video recommendation problem, this work targets at the long tail phenomena of user behavior and the sparsity of item features. A personalized compound recommendation framework for online video recommendation called Dirichlet mixture probit model for information scarcity (DPIS) is hence proposed. Assuming that each clicking sample is generated from a representation of user preferences, DPIS models the sample level topic proportions as a multinomial item vector, and utilizes topical clustering on the user part for recommendation through a probit classifier. As demonstrated by the real-world application, the proposed DPIS achieves better performance in accuracy, perplexity as well as diversity in coverage than traditional methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

Automatic stress-relieving music recommendation system based on photoplethysmography-derived heart rate variability analysis.

PubMed

Shin, Il-Hyung; Cha, Jaepyeong; Cheon, Gyeong Woo; Lee, Choonghee; Lee, Seung Yup; Yoon, Hyung-Jin; Kim, Hee Chan

2014-01-01

This paper presents an automatic stress-relieving music recommendation system (ASMRS) for individual music listeners. The ASMRS uses a portable, wireless photoplethysmography module with a finger-type sensor, and a program that translates heartbeat signals from the sensor to the stress index. The sympathovagal balance index (SVI) was calculated from heart rate variability to assess the user's stress levels while listening to music. Twenty-two healthy volunteers participated in the experiment. The results have shown that the participants' SVI values are highly correlated with their prespecified music preferences. The sensitivity and specificity of the favorable music classification also improved as the number of music repetitions increased to 20 times. Based on the SVI values, the system automatically recommends favorable music lists to relieve stress for individuals.

Development and Deployment Assessment of a Melt-Down Proof Modular Micro Reactor (MDP-MMR)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hawari, Ayman I.; Venneri, Francesco

The objective of this project is to perform feasibility assessment and technology gap analysis and establish a development roadmap for an innovative and highly compact Micro Modular Reactor (MMR) concept that integrates power production, power conversion and electricity generation in a single unit. The MMR is envisioned to use fully ceramic micro-encapsulated (FCM) fuel, a particularly robust form of TRISO fuel, and to be gas-cooled (e.g., He or CO 2) and capable of generating power in the range of 10 to 40 MW-thermal. It is designed to be absolutely melt-down proof (MDP) under all circumstances including complete loss of coolantmore » scenarios with no possible release of radioactive material, to be factory produced, to have a cycle length of greater than 20 years, and to be highly proliferation resistant. In addition, it will be transportable, retrievable and suitable for use in remote areas. As such, the MDP-MMR will represent a versatile reactor concept that is suitable for use in various applications including electricity generation, process heat utilization and propulsion.« less

(n, N) type maintenance policy for multi-component systems with failure interactions

NASA Astrophysics Data System (ADS)

Zhang, Zhuoqi; Wu, Su; Li, Binfeng; Lee, Seungchul

2015-04-01

This paper studies maintenance policies for multi-component systems in which failure interactions and opportunistic maintenance (OM) involve. This maintenance problem can be formulated as a Markov decision process (MDP). However, since an action set and state space in MDP exponentially expand as the number of components increase, traditional approaches are computationally intractable. To deal with curse of dimensionality, we decompose such a multi-component system into mutually influential single-component systems. Each single-component system is formulated as an MDP with the objective of minimising its long-run average maintenance cost. Under some reasonable assumptions, we prove the existence of the optimal (n, N) type policy for a single-component system. An algorithm to obtain the optimal (n, N) type policy is also proposed. Based on the proposed algorithm, we develop an iterative approximation algorithm to obtain an acceptable maintenance policy for a multi-component system. Numerical examples find that failure interactions and OM pose significant effects on a maintenance policy.

Creating More Credible and Persuasive Recommender Systems: The Influence of Source Characteristics on Recommender System Evaluations

NASA Astrophysics Data System (ADS)

Yoo, Kyung-Hyan; Gretzel, Ulrike

Whether users are likely to accept the recommendations provided by a recommender system is of utmost importance to system designers and the marketers who implement them. By conceptualizing the advice seeking and giving relationship as a fundamentally social process, important avenues for understanding the persuasiveness of recommender systems open up. Specifically, research regarding the influence of source characteristics, which is abundant in the context of humanhuman relationships, can provide an important framework for identifying potential influence factors. This chapter reviews the existing literature on source characteristics in the context of human-human, human-computer, and human-recommender system interactions. It concludes that many social cues that have been identified as influential in other contexts have yet to be implemented and tested with respect to recommender systems. Implications for recommender system research and design are discussed.

Pavement maintenance optimization model using Markov Decision Processes

NASA Astrophysics Data System (ADS)

Mandiartha, P.; Duffield, C. F.; Razelan, I. S. b. M.; Ismail, A. b. H.

2017-09-01

This paper presents an optimization model for selection of pavement maintenance intervention using a theory of Markov Decision Processes (MDP). There are some particular characteristics of the MDP developed in this paper which distinguish it from other similar studies or optimization models intended for pavement maintenance policy development. These unique characteristics include a direct inclusion of constraints into the formulation of MDP, the use of an average cost method of MDP, and the policy development process based on the dual linear programming solution. The limited information or discussions that are available on these matters in terms of stochastic based optimization model in road network management motivates this study. This paper uses a data set acquired from road authorities of state of Victoria, Australia, to test the model and recommends steps in the computation of MDP based stochastic optimization model, leading to the development of optimum pavement maintenance policy.

Diagnostic role of (99)Tc(m)-MDP SPECT/CT combined SPECT/MRI Multi modality imaging for early and atypical bone metastases.

PubMed

Chen, Xiao-Liang; Li, Qian; Cao, Lin; Jiang, Shi-Xi

2014-01-01

The bone metastasis appeared early before the bone imaging for most of the above patients. (99)Tc(m)-MDP ((99)Tc(m) marked methylene diphosphonate) bone imaging could diagnosis the bone metastasis with highly sensitivity, but with lower specificity. The aim of this study is to explore the diagnostic value of (99)Tc(m)-MDP SPECT/CT combined SPECT/MRI Multi modality imaging for the early period atypical bone metastases. 15 to 30 mCi (99)Tc(m)-MDP was intravenously injected to the 34 malignant patients diagnosed as doubtful early bone metastases. SPECT, CT and SPECT/CT images were captured and analyzed consequently. For the patients diagnosed as early period atypical bone metastases by SPECT/CT, combining the SPECT/CT and MRI together as the SPECT/MRI integrated image. The obtained SPECT/MRI image was analyzed and compared with the pathogenic results of patients. The results indicated that 34 early period doubtful metastatic focus, including 34 SPECT positive focus, 17 focus without special changes by using CT method, 11 bone metastases focus by using SPECT/CT method, 23 doubtful bone metastases focus, 8 doubtful bone metastases focus, 14 doubtful bone metastases focus and 2 focus without clear image. Totally, SPECT/CT combined with SPECT/MRI method diagnosed 30 bone metastatic focus and 4 doubtfully metastatic focus. In conclusion, (99)Tc(m)-MDP SPECT/CT combined SPECT/MRI Multi modality imaging shows a higher diagnostic value for the early period bone metastases, which also enhances the diagnostic accuracy rate.

Social Network Supported Process Recommender System

PubMed Central

Ye, Yanming; Yin, Jianwei; Xu, Yueshen

2014-01-01

Process recommendation technologies have gained more and more attention in the field of intelligent business process modeling to assist the process modeling. However, most of the existing technologies only use the process structure analysis and do not take the social features of processes into account, while the process modeling is complex and comprehensive in most situations. This paper studies the feasibility of social network research technologies on process recommendation and builds a social network system of processes based on the features similarities. Then, three process matching degree measurements are presented and the system implementation is discussed subsequently. Finally, experimental evaluations and future works are introduced. PMID:24672309

Social network supported process recommender system.

PubMed

Ye, Yanming; Yin, Jianwei; Xu, Yueshen

2014-01-01

Process recommendation technologies have gained more and more attention in the field of intelligent business process modeling to assist the process modeling. However, most of the existing technologies only use the process structure analysis and do not take the social features of processes into account, while the process modeling is complex and comprehensive in most situations. This paper studies the feasibility of social network research technologies on process recommendation and builds a social network system of processes based on the features similarities. Then, three process matching degree measurements are presented and the system implementation is discussed subsequently. Finally, experimental evaluations and future works are introduced.

Detection of abnormal item based on time intervals for recommender systems.

PubMed

Gao, Min; Yuan, Quan; Ling, Bin; Xiong, Qingyu

2014-01-01

With the rapid development of e-business, personalized recommendation has become core competence for enterprises to gain profits and improve customer satisfaction. Although collaborative filtering is the most successful approach for building a recommender system, it suffers from "shilling" attacks. In recent years, the research on shilling attacks has been greatly improved. However, the approaches suffer from serious problem in attack model dependency and high computational cost. To solve the problem, an approach for the detection of abnormal item is proposed in this paper. In the paper, two common features of all attack models are analyzed at first. A revised bottom-up discretized approach is then proposed based on time intervals and the features for the detection. The distributions of ratings in different time intervals are compared to detect anomaly based on the calculation of chi square distribution (χ(2)). We evaluated our approach on four types of items which are defined according to the life cycles of these items. The experimental results show that the proposed approach achieves a high detection rate with low computational cost when the number of attack profiles is more than 15. It improves the efficiency in shilling attacks detection by narrowing down the suspicious users.

Learning material recommendation based on case-based reasoning similarity scores

NASA Astrophysics Data System (ADS)

Masood, Mona; Mokmin, Nur Azlina Mohamed

2017-10-01

A personalized learning material recommendation is important in any Intelligent Tutoring System (ITS). Case-based Reasoning (CBR) is an Artificial Intelligent Algorithm that has been widely used in the development of ITS applications. This study has developed an ITS application that applied the CBR algorithm in the development process. The application has the ability to recommend the most suitable learning material to the specific student based on information in the student profile. In order to test the ability of the application in recommending learning material, two versions of the application were created. The first version displayed the most suitable learning material and the second version displayed the least preferable learning material. The results show the application has successfully assigned the students to the most suitable learning material.

Diffusion-based recommendation with trust relations on tripartite graphs

NASA Astrophysics Data System (ADS)

Wang, Ximeng; Liu, Yun; Zhang, Guangquan; Xiong, Fei; Lu, Jie

2017-08-01

The diffusion-based recommendation approach is a vital branch in recommender systems, which successfully applies physical dynamics to make recommendations for users on bipartite or tripartite graphs. Trust links indicate users’ social relations and can provide the benefit of reducing data sparsity. However, traditional diffusion-based algorithms only consider rating links when making recommendations. In this paper, the complementarity of users’ implicit and explicit trust is exploited, and a novel resource-allocation strategy is proposed, which integrates these two kinds of trust relations on tripartite graphs. Through empirical studies on three benchmark datasets, our proposed method obtains better performance than most of the benchmark algorithms in terms of accuracy, diversity and novelty. According to the experimental results, our method is an effective and reasonable way to integrate additional features into the diffusion-based recommendation approach.

Introduction on health recommender systems.

PubMed

Sanchez-Bocanegra, C L; Sanchez-Laguna, F; Sevillano, J L

2015-01-01

People are looking for appropriate health information which they are concerned about. The Internet is a great resource of this kind of information, but we have to be careful if we don't want to get harmful info. Health recommender systems are becoming a new wave for apt health information as systems suggest the best data according to the patients' needs.The main goals of health recommender systems are to retrieve trusted health information from the Internet, to analyse which is suitable for the user profile and select the best that can be recommended, to adapt their selection methods according to the knowledge domain and to learn from the best recommendations.A brief definition of recommender systems will be given and an explanation of how are they incorporated in the health sector. A description of the main elementary recommender methods as well as their most important problems will also be made. And, to finish, the state of the art will be described.

Improved personalized recommendation based on a similarity network

NASA Astrophysics Data System (ADS)

Wang, Ximeng; Liu, Yun; Xiong, Fei

2016-08-01

A recommender system helps individual users find the preferred items rapidly and has attracted extensive attention in recent years. Many successful recommendation algorithms are designed on bipartite networks, such as network-based inference or heat conduction. However, most of these algorithms define the resource-allocation methods for an average allocation. That is not reasonable because average allocation cannot indicate the user choice preference and the influence between users which leads to a series of non-personalized recommendation results. We propose a personalized recommendation approach that combines the similarity function and bipartite network to generate a similarity network that improves the resource-allocation process. Our model introduces user influence into the recommender system and states that the user influence can make the resource-allocation process more reasonable. We use four different metrics to evaluate our algorithms for three benchmark data sets. Experimental results show that the improved recommendation on a similarity network can obtain better accuracy and diversity than some competing approaches.

Recommendation based on trust diffusion model.

PubMed

Yuan, Jinfeng; Li, Li

2014-01-01

Recommender system is emerging as a powerful and popular tool for online information relevant to a given user. The traditional recommendation system suffers from the cold start problem and the data sparsity problem. Many methods have been proposed to solve these problems, but few can achieve satisfactory efficiency. In this paper, we present a method which combines the trust diffusion (DiffTrust) algorithm and the probabilistic matrix factorization (PMF). DiffTrust is first used to study the possible diffusions of trust between various users. It is able to make use of the implicit relationship of the trust network, thus alleviating the data sparsity problem. The probabilistic matrix factorization (PMF) is then employed to combine the users' tastes with their trusted friends' interests. We evaluate the algorithm on Flixster, Moviedata, and Epinions datasets, respectively. The experimental results show that the recommendation based on our proposed DiffTrust + PMF model achieves high performance in terms of the root mean square error (RMSE), Recall, and F Measure.

Recommendation Based on Trust Diffusion Model

PubMed Central

Li, Li

2014-01-01

Recommender system is emerging as a powerful and popular tool for online information relevant to a given user. The traditional recommendation system suffers from the cold start problem and the data sparsity problem. Many methods have been proposed to solve these problems, but few can achieve satisfactory efficiency. In this paper, we present a method which combines the trust diffusion (DiffTrust) algorithm and the probabilistic matrix factorization (PMF). DiffTrust is first used to study the possible diffusions of trust between various users. It is able to make use of the implicit relationship of the trust network, thus alleviating the data sparsity problem. The probabilistic matrix factorization (PMF) is then employed to combine the users' tastes with their trusted friends' interests. We evaluate the algorithm on Flixster, Moviedata, and Epinions datasets, respectively. The experimental results show that the recommendation based on our proposed DiffTrust + PMF model achieves high performance in terms of the root mean square error (RMSE), Recall, and F Measure. PMID:25009827

Detection of breast cancer microcalcification using (99m)Tc-MDP SPECT or Osteosense 750EX FMT imaging.

PubMed

Felix, Dayo D; Gore, John C; Yankeelov, Thomas E; Peterson, Todd E; Barnes, Stephanie; Whisenant, Jennifer; Weis, Jared; Shoukouhi, Sepideh; Virostko, John; Nickels, Michael; McIntyre, J Oliver; Sanders, Melinda; Abramson, Vandana; Tantawy, Mohammed N

2015-03-01

In previous work, we demonstrated the presence of hydroxyapetite (type II microcalcification), HAP, in triple negative MDA-MB-231 breast cancer cells. We used (18)F-NaF to detect these types of cancers in mouse models as the free fluorine, (18)F(-), binds to HAP similar to bone uptake. In this work, we investigate other bone targeting agents and techniques including (99m)Tc-MDP SPECT and Osteosense 750EX FMT imaging as alternatives for breast cancer diagnosis via targeting HAP within the tumor microenvironment. Thirteen mice were injected subcutaneously in the right flank with 10(6) MDA-MB-231 cells. When the tumor size reached ~0.6 cm(3), mice (n=9) were injected with ~37 MBq of (99m)Tc-MDP intravenously and then imaged one hour later in a NanoSPECT/CT or injected intravenously with 4 nmol/g of Osetosense 750EX and imaged 24 hours later in an FMT (n=4). The imaging probe concentration in the tumor was compared to that of muscle. Following SPECT imaging, the tumors were harvested, sectioned into 10 μm slices, and underwent autoradiography or von Kossa staining to correlate (99m)Tc-MDP binding with HAP distribution within the tumor. The SPECT images were normalized to the injected dose and regions-of-interest (ROIs) were drawn around bone, tumor, and muscle to obtain the radiotracer concentration in these regions in units of percent injected dose per unit volume. ROIs were drawn around bone and tumor in the FMT images as no FMT signal was observed in normal muscle. Uptake of (99m)Tc-MDP was observed in the bone and tumor with little or no uptake in the muscle with concentrations of 11.34±1.46 (mean±SD), 2.22±0.95, and 0.05±0.04%ID/cc, respectively. Uptake of Osteosense 750EX was also observed in the bone and tumor with concentrations of 0.35±0.07 (mean±SD) and 0.04±0.01picomoles, respectively. No FMT signal was observed in the normal muscle. There was no significant difference in the bone-to-tumor ratio between the two modalities (5.1±2.3 for SPECT and 8.8�

A Personalized Electronic Movie Recommendation System Based on Support Vector Machine and Improved Particle Swarm Optimization

PubMed Central

Wang, Xibin; Luo, Fengji; Qian, Ying; Ranzi, Gianluca

2016-01-01

With the rapid development of ICT and Web technologies, a large an amount of information is becoming available and this is producing, in some instances, a condition of information overload. Under these conditions, it is difficult for a person to locate and access useful information for making decisions. To address this problem, there are information filtering systems, such as the personalized recommendation system (PRS) considered in this paper, that assist a person in identifying possible products or services of interest based on his/her preferences. Among available approaches, collaborative Filtering (CF) is one of the most widely used recommendation techniques. However, CF has some limitations, e.g., the relatively simple similarity calculation, cold start problem, etc. In this context, this paper presents a new regression model based on the support vector machine (SVM) classification and an improved PSO (IPSO) for the development of an electronic movie PRS. In its implementation, a SVM classification model is first established to obtain a preliminary movie recommendation list based on which a SVM regression model is applied to predict movies’ ratings. The proposed PRS not only considers the movie’s content information but also integrates the users’ demographic and behavioral information to better capture the users’ interests and preferences. The efficiency of the proposed method is verified by a series of experiments based on the MovieLens benchmark data set. PMID:27898691

A Personalized Electronic Movie Recommendation System Based on Support Vector Machine and Improved Particle Swarm Optimization.

PubMed

Wang, Xibin; Luo, Fengji; Qian, Ying; Ranzi, Gianluca

2016-01-01

With the rapid development of ICT and Web technologies, a large an amount of information is becoming available and this is producing, in some instances, a condition of information overload. Under these conditions, it is difficult for a person to locate and access useful information for making decisions. To address this problem, there are information filtering systems, such as the personalized recommendation system (PRS) considered in this paper, that assist a person in identifying possible products or services of interest based on his/her preferences. Among available approaches, collaborative Filtering (CF) is one of the most widely used recommendation techniques. However, CF has some limitations, e.g., the relatively simple similarity calculation, cold start problem, etc. In this context, this paper presents a new regression model based on the support vector machine (SVM) classification and an improved PSO (IPSO) for the development of an electronic movie PRS. In its implementation, a SVM classification model is first established to obtain a preliminary movie recommendation list based on which a SVM regression model is applied to predict movies' ratings. The proposed PRS not only considers the movie's content information but also integrates the users' demographic and behavioral information to better capture the users' interests and preferences. The efficiency of the proposed method is verified by a series of experiments based on the MovieLens benchmark data set.

Finding Your Literature Match - A Physics Literature Recommender System

NASA Astrophysics Data System (ADS)

Henneken, Edwin; Kurtz, Michael

2010-03-01

A recommender system is a filtering algorithm that helps you find the right match by offering suggestions based on your choices and information you have provided. A latent factor model is a successful approach. Here an item is characterized by a vector describing to what extent a product is described by each of N categories, and a person is characterized by an ``interest'' vector, based on explicit or implicit feedback by this user. The recommender system assigns ratings to new items and suggests items this user might be interested in. Here we present results of a recommender system designed to find recent literature of interest to people working in the field of solid state physics. Since we do not have explicit feedback, our user vector consists of (implicit) ``usage.'' Using a system of N keywords we construct normalized keyword vectors for articles based on the keywords of that article and its bibliography. The normalized ``interest'' vector is created by calculating the normalized frequency of keyword occurrence in the papers cited by the papers read.

Comparison of 99mTc-MDP SPECT qualitative vs quantitative results in patients with suspected condylar hyperplasia.

PubMed

López Buitrago, D F; Ruiz Botero, J; Corral, C M; Carmona, A R; Sabogal, A

To compare qualitative vs quantitative results of Single Photon Emission Computerised Tomography (SPECT), calculated from percentage of 99m Tc-MDP (methylene diphosphonate) uptake, in condyles of patients with a presumptive clinical diagnosis of condylar hyperplasia. A retrospective, descriptive study was conducted on the 99m Tc-MDP SPECT bone scintigraphy reports from 51 patients, with clinical impression of facial asymmetry related to condylar hyperplasia referred by their specialist in orthodontics or maxillofacial surgery, to a nuclear medicine department in order to take this type of test. Quantitative data from 99m Tc-MDP condylar uptake of each were obtained and compared with qualitative image interpretation reported by a nuclear medicine expert. The concordances between the 51 qualitative and quantitative reports results was established. The total sample included 32 women (63%) and 19 men (37%). The patient age range was 13-45 years (21±8 years). According to qualitative reports, 19 patients were positive for right side condylar hyperplasia, 12 for left side condylar hyperplasia, with 8 bilateral, and 12 negative. The quantitative reports diagnosed 16 positives for right side condylar hyperplasia, 10 for left side condylar hyperplasia, and 25 negatives. Nuclear medicine images are an important diagnostic tool, but the qualitative interpretation of the images is not as reliable as the quantitative calculation. The agreement between the two types of report is low (39.2%, Kappa=0.13; P>.2). The main limitation of quantitative reports is that they do not register bilateral condylar hyperplasia cases. Copyright © 2017 Elsevier España, S.L.U. y SEMNIM. All rights reserved.

Development of the Parkland-UT Southwestern Colonoscopy Reporting System (CoRS) for evidence-based colon cancer surveillance recommendations

PubMed Central

Gupta, Samir; Halm, Ethan A; Wright, Shaun; McCallister, Katharine; Bishop, Wendy; Santini, Noel; Mayorga, Christian; Agrawal, Deepak; Moran, Brett; Sanders, Joanne M; Singal, Amit G

2016-01-01

Objective Through colonoscopy, polyps can be identified and removed to reduce colorectal cancer incidence and mortality. Appropriate use of surveillance colonoscopy, post polypectomy, is a focus of healthcare reform. Materials and Methods The authors developed and implemented the first electronic medical record–based colonoscopy reporting system (CoRS) that matches endoscopic findings with guideline-consistent surveillance recommendations and generates tailored results and recommendation letters for patients and providers. Results In its first year, CoRS was used in 98.6% of indicated cases. Via a survey, colonoscopists agreed/strongly agreed it is easy to use (83%), provides guideline-based recommendations (89%), improves quality of Spanish letters (94%), they would recommend it for other institutions (78%), and it made their work easier (61%), and led to improved practice (56%). Discussion CoRS’ widespread adoption and acceptance likely resulted from stakeholder engagement throughout the development and implementation process. Conclusion CoRS is well-accepted by clinicians and provides guideline-based recommendations and results communications to patients and providers. PMID:26254481

Recommended system of application and development

NASA Astrophysics Data System (ADS)

Wang, Wei

2018-04-01

A recommender system is a project that helps users identify their wishes and needs. The recommender system has been successfully applied to many e-commerce environments, such as news, film, music, books and other areas of recommendation. This paper mainly discusses the application of recommendation technology in software engineering, data and knowledge engineering, configurable projects and persuasion technology, and summarizes the development trend of recommendation technology in the future.

An effective trust-based recommendation method using a novel graph clustering algorithm

NASA Astrophysics Data System (ADS)

Moradi, Parham; Ahmadian, Sajad; Akhlaghian, Fardin

2015-10-01

Recommender systems are programs that aim to provide personalized recommendations to users for specific items (e.g. music, books) in online sharing communities or on e-commerce sites. Collaborative filtering methods are important and widely accepted types of recommender systems that generate recommendations based on the ratings of like-minded users. On the other hand, these systems confront several inherent issues such as data sparsity and cold start problems, caused by fewer ratings against the unknowns that need to be predicted. Incorporating trust information into the collaborative filtering systems is an attractive approach to resolve these problems. In this paper, we present a model-based collaborative filtering method by applying a novel graph clustering algorithm and also considering trust statements. In the proposed method first of all, the problem space is represented as a graph and then a sparsest subgraph finding algorithm is applied on the graph to find the initial cluster centers. Then, the proposed graph clustering algorithm is performed to obtain the appropriate users/items clusters. Finally, the identified clusters are used as a set of neighbors to recommend unseen items to the current active user. Experimental results based on three real-world datasets demonstrate that the proposed method outperforms several state-of-the-art recommender system methods.

A Decision Fusion Framework for Treatment Recommendation Systems.

PubMed

Mei, Jing; Liu, Haifeng; Li, Xiang; Xie, Guotong; Yu, Yiqin

2015-01-01

Treatment recommendation is a nontrivial task--it requires not only domain knowledge from evidence-based medicine, but also data insights from descriptive, predictive and prescriptive analysis. A single treatment recommendation system is usually trained or modeled with a limited (size or quality) source. This paper proposes a decision fusion framework, combining both knowledge-driven and data-driven decision engines for treatment recommendation. End users (e.g. using the clinician workstation or mobile apps) could have a comprehensive view of various engines' opinions, as well as the final decision after fusion. For implementation, we leverage several well-known fusion algorithms, such as decision templates and meta classifiers (of logistic and SVM, etc.). Using an outcome-driven evaluation metric, we compare the fusion engine with base engines, and our experimental results show that decision fusion is a promising way towards a more valuable treatment recommendation.

Ubiquitous Multicriteria Clinic Recommendation System.

PubMed

Chen, Toly

2016-05-01

Advancements in information, communication, and sensor technologies have led to new opportunities in medical care and education. Patients in general prefer visiting the nearest clinic, attempt to avoid waiting for treatment, and have unequal preferences for different clinics and doctors. Therefore, to enable patients to compare multiple clinics, this study proposes a ubiquitous multicriteria clinic recommendation system. In this system, patients can send requests through their cell phones to the system server to obtain a clinic recommendation. Once the patient sends this information to the system, the system server first estimates the patient's speed according to the detection results of a global positioning system. It then applies a fuzzy integer nonlinear programming-ordered weighted average approach to assess four criteria and finally recommends a clinic with maximal utility to the patient. The proposed methodology was tested in a field experiment, and the experimental results showed that it is advantageous over two existing methods in elevating the utilities of recommendations. In addition, such an advantage was shown to be statistically significant.

The Effect of Recommendation Systems on Internet-Based Learning for Different Learners: A Data Mining Analysis

ERIC Educational Resources Information Center

Liu, Chen-Chung; Chang, Chia-Jung; Tseng, Jui-Min

2013-01-01

A general challenge facing Internet-based learners is how to identify information objects which are helpful in expanding their understanding of important information in a domain. Recommendation systems may assist learners in identifying potentially helpful information objects. However, the recent literature mainly focuses on the technical…

Uncovering the information core in recommender systems

NASA Astrophysics Data System (ADS)

Zeng, Wei; Zeng, An; Liu, Hao; Shang, Ming-Sheng; Zhou, Tao

2014-08-01

With the rapid growth of the Internet and overwhelming amount of information that people are confronted with, recommender systems have been developed to effectively support users' decision-making process in online systems. So far, much attention has been paid to designing new recommendation algorithms and improving existent ones. However, few works considered the different contributions from different users to the performance of a recommender system. Such studies can help us improve the recommendation efficiency by excluding irrelevant users. In this paper, we argue that in each online system there exists a group of core users who carry most of the information for recommendation. With them, the recommender systems can already generate satisfactory recommendation. Our core user extraction method enables the recommender systems to achieve 90% of the accuracy of the top-L recommendation by taking only 20% of the users into account. A detailed investigation reveals that these core users are not necessarily the large-degree users. Moreover, they tend to select high quality objects and their selections are well diversified.

PERSON-Personalized Expert Recommendation System for Optimized Nutrition.

PubMed

Chen, Chih-Han; Karvela, Maria; Sohbati, Mohammadreza; Shinawatra, Thaksin; Toumazou, Christofer

2018-02-01

The rise of personalized diets is due to the emergence of nutrigenetics and genetic tests services. However, the recommendation system is far from mature to provide personalized food suggestion to consumers for daily usage. The main barrier of connecting genetic information to personalized diets is the complexity of data and the scalability of the applied systems. Aiming to cross such barriers and provide direct applications, a personalized expert recommendation system for optimized nutrition is introduced in this paper, which performs direct to consumer personalized grocery product filtering and recommendation. Deep learning neural network model is applied to achieve automatic product categorization. The ability of scaling with unknown new data is achieved through the generalized representation of word embedding. Furthermore, the categorized products are filtered with a model based on individual genetic data with associated phenotypic information and a case study with databases from three different sources is carried out to confirm the system.

I should not recommend it to you even if you will like it: the ethics of recommender systems

NASA Astrophysics Data System (ADS)

Tang, Tiffany Ya; Winoto, Pinata

2016-01-01

In this paper, we extend the current research in the recommendation system community by showing that users did attach ethical consideration to items. In an experiment (N = 111) that manipulated several moral factors regarding the potentially harmful content in movies, books, and games, users were asked to evaluate the appropriateness of recommending these items to teenagers and adult couples. Results agreed with previous studies in that gender plays a key role in making moral judgment, especially regarding the ethical appropriateness of an item. The pilot study further identifies degrees of aversion regarding the appeal of these elements in media for ethical recommendations. Based on the study, we propose a user-initiated ethical recommender system to help users pick up morally appropriate items during the post-recommendation process. We believe that the ethical appropriateness of items perceived by end users could predict the trust and credibility of the system.

Hierarchically Structured Recommender System for Improving NPS

ERIC Educational Resources Information Center

Kuang, Jieyan

2016-01-01

Net Promoter System (NPS) is well known as an evaluation measure of the growth engine of big companies in the business area. The ultimate goal of my research is to build an action rules and meta-actions based recommender system for improving NPS scores of 34 companies (clients) dealing with similar businesses in the US and Canada. With the given…

Context-Awareness Based Personalized Recommendation of Anti-Hypertension Drugs.

PubMed

Chen, Dexin; Jin, Dawei; Goh, Tiong-Thye; Li, Na; Wei, Leiru

2016-09-01

The World Health Organization estimates that almost one-third of the world's adult population are suffering from hypertension which has gradually become a "silent killer". Due to the varieties of anti-hypertensive drugs, patients are interested in how these drugs can be selected to match their respective conditions. This study provides a personalized recommendation service system of anti-hypertensive drugs based on context-awareness and designs a context ontology framework of the service. In addition, this paper introduces a Semantic Web Rule Language (SWRL)-based rule to provide high-level context reasoning and information recommendation and to overcome the limitation of ontology reasoning. To make the information recommendation of the drugs more personalized, this study also devises three categories of information recommendation rules that match different priority levels and uses a ranking algorithm to optimize the recommendation. The experiment conducted shows that combining the anti-hypertensive drugs personalized recommendation service context ontology (HyRCO) with the optimized rule reasoning can achieve a higher-quality personalized drug recommendation service. Accordingly this exploratory study of the personalized recommendation service for hypertensive drugs and its method can be easily adopted for other diseases.

Personalised news filtering and recommendation system using Chi-square statistics-based K-nearest neighbour (χ2SB-KNN) model

NASA Astrophysics Data System (ADS)

Adeniyi, D. A.; Wei, Z.; Yang, Y.

2017-10-01

Recommendation problem has been extensively studied by researchers in the field of data mining, database and information retrieval. This study presents the design and realisation of an automated, personalised news recommendations system based on Chi-square statistics-based K-nearest neighbour (χ2SB-KNN) model. The proposed χ2SB-KNN model has the potential to overcome computational complexity and information overloading problems, reduces runtime and speeds up execution process through the use of critical value of χ2 distribution. The proposed recommendation engine can alleviate scalability challenges through combined online pattern discovery and pattern matching for real-time recommendations. This work also showcases the development of a novel method of feature selection referred to as Data Discretisation-Based feature selection method. This is used for selecting the best features for the proposed χ2SB-KNN algorithm at the preprocessing stage of the classification procedures. The implementation of the proposed χ2SB-KNN model is achieved through the use of a developed in-house Java program on an experimental website called OUC newsreaders' website. Finally, we compared the performance of our system with two baseline methods which are traditional Euclidean distance K-nearest neighbour and Naive Bayesian techniques. The result shows a significant improvement of our method over the baseline methods studied.

Improved Personalized Recommendation Based on Causal Association Rule and Collaborative Filtering

ERIC Educational Resources Information Center

Lei, Wu; Qing, Fang; Zhou, Jin

2016-01-01

There are usually limited user evaluation of resources on a recommender system, which caused an extremely sparse user rating matrix, and this greatly reduce the accuracy of personalized recommendation, especially for new users or new items. This paper presents a recommendation method based on rating prediction using causal association rules.…

EULAR evidence‐based recommendations on the management of systemic glucocorticoid therapy in rheumatic diseases

PubMed Central

Hoes, J N; Jacobs, J W G; Boers, M; Boumpas, D; Buttgereit, F; Caeyers, N; Choy, E H; Cutolo, M; Da Silva, J A P; Esselens, G; Guillevin, L; Hafstrom, I; Kirwan, J R; Rovensky, J; Russell, A; Saag, K G; Svensson, B; Westhovens, R; Zeidler, H; Bijlsma, J W J

2007-01-01

Objective To develop evidence‐based recommendations for the management of systemic glucocorticoid (GC) therapy in rheumatic diseases. Methods The multidisciplinary guideline development group from 11 European countries, Canada and the USA consisted of 15 rheumatologists, 1 internist, 1 rheumatologist–epidemiologist, 1 health professional, 1 patient and 1 research fellow. The Delphi method was used to agree on 10 key propositions related to the safe use of GCs. A systematic literature search of PUBMED, EMBASE, CINAHL, and Cochrane Library was then used to identify the best available research evidence to support each of the 10 propositions. The strength of recommendation was given according to research evidence, clinical expertise and perceived patient preference. Results The 10 propositions were generated through three Delphi rounds and included patient education, risk factors, adverse effects, concomitant therapy (ie, non‐steroidal anti‐inflammatory drugs, gastroprotection and cyclo‐oxygenase‐2 selective inhibitors, calcium and vitamin D, bisphosphonates) and special safety advice (ie, adrenal insufficiency, pregnancy, growth impairment). Conclusion Ten key recommendations for the management of systemic GC‐therapy were formulated using a combination of systematically retrieved research evidence and expert consensus. There are areas of importance that have little evidence (ie, dosing and tapering strategies, timing, risk factors and monitoring for adverse effects, perioperative GC‐replacement) and need further research; therefore also a research agenda was composed. PMID:17660219

Enabling Open Research Data Discovery through a Recommender System

NASA Astrophysics Data System (ADS)

Devaraju, Anusuriya; Jayasinghe, Gaya; Klump, Jens; Hogan, Dominic

2017-04-01

Government agencies, universities, research and nonprofit organizations are increasingly publishing their datasets to promote transparency, induce new research and generate economic value through the development of new products or services. The datasets may be downloaded from various data portals (data repositories) which are general or domain-specific. The Registry of Research Data Repository (re3data.org) lists more than 2500 such data repositories from around the globe. Data portals allow keyword search and faceted navigation to facilitate discovery of research datasets. However, the volume and variety of datasets have made finding relevant datasets more difficult. Common dataset search mechanisms may be time consuming, may produce irrelevant results and are primarily suitable for users who are familiar with the general structure and contents of the respective database. Therefore, we need new approaches to support research data discovery. Recommender systems offer new possibilities for users to find datasets that are relevant to their research interests. This study presents a recommender system developed for the CSIRO Data Access Portal (DAP, http://data.csiro.au). The datasets hosted on the portal are diverse, published by researchers from 13 business units in the organisation. The goal of the study is not to replace the current search mechanisms on the data portal, but rather to extend the data discovery through an exploratory search, in this case by building a recommender system. We adopted a hybrid recommendation approach, comprising content-based filtering and item-item collaborative filtering. The content-based filtering computes similarities between datasets based on metadata such as title, keywords, descriptions, fields of research, location, contributors, etc. The collaborative filtering utilizes user search behaviour and download patterns derived from the server logs to determine similar datasets. Similarities above are then combined with different

Personalized Location-Based Recommendation Services for Tour Planning in Mobile Tourism Applications

NASA Astrophysics Data System (ADS)

Yu, Chien-Chih; Chang, Hsiao-Ping

Travel and tour planning is a process of searching, selecting, grouping and sequencing destination related products and services including attractions, accommodations, restaurants, and activities. Personalized recommendation services aim at suggesting products and services to meet users’ preferences and needs, while location-based services focus on providing information based on users’ current positions. Due to the fast growing of user needs in the mobile tourism domain, how to provide personalized location-based tour recommendation services becomes a critical research and practical issue. The objective of this paper is to propose a system architecture and design methods for facilitating the delivery of location-based recommendation services to support personalized tour planning. Based on tourists’ current location and time, as well as personal preferences and needs, various recommendations regarding sightseeing spots, hotels, restaurants, and packaged tour plans can be generated efficiently. An application prototype is also implemented to illustrate and test the system feasibility and effectiveness.

The uptake by the canine tibia of the bone-scanning agent 99mTc-MDP before and after an osteotomy.

PubMed

Hughes, S; Khan, R; Davies, R; Lavender, P

1978-11-01

The residue and extraction of technetium-labelled methylene diphosphonate (99mTc-MDP), a substance used in bone scanning, was examined in the canine tibia and found to be low. Examination of washout curves suggested that there were four compartments in cortical bone, a vascular, a perivascular, a bone fluid and a bone compartment. After an osteotomy in the canine tibia the residue of 99mTc-MDP increased. This was believed to be due to an increase in the blood supply to the bone and to an associated increase in new bone available for exchange. Bone scanning in a fracture is therefore a reflection of the vascular status of the bone being examined and of the uptake by bone. This is dependent on there being an adequate blood supply to the bone and an increased number of mineral-binding sites.

Recent developments in affective recommender systems

NASA Astrophysics Data System (ADS)

Katarya, Rahul; Verma, Om Prakash

2016-11-01

Recommender systems (RSs) are playing a significant role since 1990s as they provide relevant, personalized information to the users over the internet. Lots of work have been done in information filtering, utilization, and application related to RS. However, an important area recently draws our attention which is affective recommender system. Affective recommender system (ARS) is latest trending area of research, as publication in this domain are few and recently published. ARS is associated with human behaviour, human factors, mood, senses, emotions, facial expressions, body gesture and physiological with human-computer interaction (HCI). Due to this assortment and various interests, more explanation is required, as it is in premature phase and growing as compared to other fields. So we have done literature review (LR) in the affective recommender systems by doing classification, incorporate reputed articles published from the year 2003 to February 2016. We include articles which highlight, analyse, and perform a study on affective recommender systems. This article categorizes, synthesizes, and discusses the research and development in ARS. We have classified and managed ARS papers according to different perspectives: research gaps, nature, algorithm or method adopted, datasets, the platform on executed, types of information and evaluation techniques applied. The researchers and professionals will positively support this survey article for understanding the current position, research in affective recommender systems and will guide future trends, opportunity and research focus in ARS.

A hybrid fuzzy-ontology based intelligent system to determine level of severity and treatment recommendation for Benign Prostatic Hyperplasia.

PubMed

Torshizi, Abolfazl Doostparast; Zarandi, Mohammad Hossein Fazel; Torshizi, Ghazaleh Doostparast; Eghbali, Kamyar

2014-01-01

This paper deals with application of fuzzy intelligent systems in diagnosing severity level and recommending appropriate therapies for patients having Benign Prostatic Hyperplasia. Such an intelligent system can have remarkable impacts on correct diagnosis of the disease and reducing risk of mortality. This system captures various factors from the patients using two modules. The first module determines severity level of the Benign Prostatic Hyperplasia and the second module, which is a decision making unit, obtains output of the first module accompanied by some external knowledge and makes an appropriate treatment decision based on its ontology model and a fuzzy type-1 system. In order to validate efficiency and accuracy of the developed system, a case study is conducted by 44 participants. Then the results are compared with the recommendations of a panel of experts on the experimental data. Then precision and accuracy of the results were investigated based on a statistical analysis. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Application of the unified mask data format based on OASIS for VSB EB writers

NASA Astrophysics Data System (ADS)

Suzuki, Toshio; Hirumi, Junji; Suga, Osamu

2005-11-01

Mask data preparation (MDP) for modern mask manufacturing becomes a complex process because many kinds of EB data formats are used in mask makers and EB data files continue to become bigger by the application of RET. Therefore we developed a unified mask pattern data format named "OASIS.VSB1" and a job deck format named "MALY2" for Variable-Shaped-Beam (VSB) EB writers. OASIS.VSB is the mask pattern data format based on OASISTM 3 (Open Artwork System Interchange Standard) released as a successive format to GDSII by SEMI. We defined restrictions on OASIS for VSB EB writers to input OASIS.VSB data directly to VSB EB writers just like the native EB data. OASIS.VSB specification and MALY specification have been disclosed to the public and will become a SEMI standard in the near future. We started to promote the spread activities of OASIS.VSB and MALY. For practical use of OASIS.VSB and MALY, we are discussing the infrastructure system of MDP processing using OASIS.VSB and MALY with mask makers, VSB EB makers, and device makers. We are also discussing the tools for the infrastructure system with EDA vendors. The infrastructure system will enable TAT, the man-hour, and the cost in MDP to be reduced. In this paper, we propose the plan of the infrastructure system of MDP processing using OASIS.VSB and MALY as an application of OASIS.VSB and MALY.

TrustRank: a Cold-Start tolerant recommender system

NASA Astrophysics Data System (ADS)

Zou, Haitao; Gong, Zhiguo; Zhang, Nan; Zhao, Wei; Guo, Jingzhi

2015-02-01

The explosive growth of the World Wide Web leads to the fast advancing development of e-commerce techniques. Recommender systems, which use personalised information filtering techniques to generate a set of items suitable to a given user, have received considerable attention. User- and item-based algorithms are two popular techniques for the design of recommender systems. These two algorithms are known to have Cold-Start problems, i.e., they are unable to effectively handle Cold-Start users who have an extremely limited number of purchase records. In this paper, we develop TrustRank, a novel recommender system which handles the Cold-Start problem by leveraging the user-trust networks which are commonly available for e-commerce applications. A user-trust network is formed by friendships or trust relationships that users specify among them. While it is straightforward to conjecture that a user-trust network is helpful for improving the accuracy of recommendations, a key challenge for using user-trust network to facilitate Cold-Start users is that these users also tend to have a very limited number of trust relationships. To address this challenge, we propose a pre-processing propagation of the Cold-Start users' trust network. In particular, by applying the personalised PageRank algorithm, we expand the friends of a given user to include others with similar purchase records to his/her original friends. To make this propagation algorithm scalable to a large amount of users, as required by real-world recommender systems, we devise an iterative computation algorithm of the original personalised TrustRank which can incrementally compute trust vectors for Cold-Start users. We conduct extensive experiments to demonstrate the consistently improvement provided by our proposed algorithm over the existing recommender algorithms on the accuracy of recommendations for Cold-Start users.

Research on the Application of Persona in Book Recommendation System

NASA Astrophysics Data System (ADS)

Gao, Baozhong; Du, Shouyan; Li, Xinzhi; Liu, Fangai

2017-10-01

Currently, there still exists a host of problems in the book recommendation system, such as low accuracy, weak correlation and poor pertinence. Aiming to unravel these problems, this paper based on the theory of big data and data mining technology, through analyzing internet user behavior and the “5C” model of personal credit evaluation, combined with joint impact weight calculation method, which involves user grade, borrowing credit, book friend recommendation degree, book friend recommended adoption degree, borrowing frequency, borrowing number, and borrowing time interval. User activity and credit are also taken into account in the process of establishing user tagging system so as to build classified book recommendation service. This method is of universal meaning to the book recommendation service of smart campus with user as the core under big data environment.

(99m)Tc-MDP SPECT/CT as the one-stop imaging modality for the diagnosis of early setting of Kienbock's disease.

PubMed

Arora, S; Singh Dhull, V; Karunanithi, S; Kumar Parida, G; Sharma, A; Shamim, S A

2015-01-01

(99m)Tc-Methylene diphosphonate (MDP) triple phase bone scintigraphy (BS) has a role in early diagnosis of Kienbock's disease, especially when the X-ray is negative. Early diagnosis can result in prompt management of the patient since wrist pain in older individuals due to aging may go unnoticed or be due to other diagnoses with the production of greater damage and eventually a worse prognosis. Herein, we present a case report of a 29-year-old female with Kienbock's disease in whom the X-ray was negative and MRI incorrect. The (99m)Tc-MDP SPECT/CT BS helped the diagnosis of the disease in an early stage (stage 1) and had a clinical impact on the patient's management. Copyright © 2014 Elsevier España, S.L.U. and SEMNIM. All rights reserved.

A Recommender System in the Cyber Defense Domain

DTIC Science & Technology

2014-03-27

monitoring software is a java based program sending updates to the database on the sensor machine. The host monitoring program gathers information about...3.2.2 Database. A MySQL database located on the sensor machine acts as the storage for the sensors on the network. Snort, Nmap, vulnerability scores, and...machine with the IDS and the recommender is labeled “sensor”. The recommender system code is written in java and compiled using java version 1.6.024

User Controllability in a Hybrid Recommender System

ERIC Educational Resources Information Center

Parra Santander, Denis Alejandro

2013-01-01

Since the introduction of Tapestry in 1990, research on recommender systems has traditionally focused on the development of algorithms whose goal is to increase the accuracy of predicting users' taste based on historical data. In the last decade, this research has diversified, with "human factors" being one area that has received…

On the Recommender System for University Library

ERIC Educational Resources Information Center

Fu, Shunkai; Zhang, Yao; Seinminn

2013-01-01

Libraries are important to universities, and they have two primary features: readers as well as collections are highly professional. In this study, based on the experimental study with five millions of users' borrowing records, our discussion covers: (1) the necessity of recommender system for university libraries; (2) collaborative filtering (CF)…

Human resource recommendation algorithm based on ensemble learning and Spark

NASA Astrophysics Data System (ADS)

Cong, Zihan; Zhang, Xingming; Wang, Haoxiang; Xu, Hongjie

2017-08-01

Aiming at the problem of “information overload” in the human resources industry, this paper proposes a human resource recommendation algorithm based on Ensemble Learning. The algorithm considers the characteristics and behaviours of both job seeker and job features in the real business circumstance. Firstly, the algorithm uses two ensemble learning methods-Bagging and Boosting. The outputs from both learning methods are then merged to form user interest model. Based on user interest model, job recommendation can be extracted for users. The algorithm is implemented as a parallelized recommendation system on Spark. A set of experiments have been done and analysed. The proposed algorithm achieves significant improvement in accuracy, recall rate and coverage, compared with recommendation algorithms such as UserCF and ItemCF.

System Data Bases In European Satellites Programs: Lessons Learned and Recommendations

NASA Astrophysics Data System (ADS)

Passot, X.; Denuault, D.; Guiral, Ph.; Kerjean, L.; Lebreton, D.; Lecrvain, C.; Valera, S.

2007-08-01

This paper is intended for European space eningeers who must design or interact with all or part of a system database, hereafter referred to as 'SDB'. The document presents the objectives of a system database (SDB), describes the development of an SDB project from the specification stage until operations, and preovides recommendations so that an effective system can be obtained. As part of return on experience, the document gives an overview of exisiting systems in the European space industry. To prepare the new systems, it presents the services to be provided, the tools available and the emerging standars towards which SDBs must converge.

An Inter-Personal Information Sharing Model Based on Personalized Recommendations

NASA Astrophysics Data System (ADS)

Kamei, Koji; Funakoshi, Kaname; Akahani, Jun-Ichi; Satoh, Tetsuji

In this paper, we propose an inter-personal information sharing model among individuals based on personalized recommendations. In the proposed model, we define an information resource as shared between people when both of them consider it important --- not merely when they both possess it. In other words, the model defines the importance of information resources based on personalized recommendations from identifiable acquaintances. The proposed method is based on a collaborative filtering system that focuses on evaluations from identifiable acquaintances. It utilizes both user evaluations for documents and their contents. In other words, each user profile is represented as a matrix of credibility to the other users' evaluations on each domain of interests. We extended the content-based collaborative filtering method to distinguish other users to whom the documents should be recommended. We also applied a concept-based vector space model to represent the domain of interests instead of the previous method which represented them by a term-based vector space model. We introduce a personalized concept-base compiled from each user's information repository to improve the information retrieval in the user's environment. Furthermore, the concept-spaces change from user to user since they reflect the personalities of the users. Because of different concept-spaces, the similarity between a document and a user's interest varies for each user. As a result, a user receives recommendations from other users who have different view points, achieving inter-personal information sharing based on personalized recommendations. This paper also describes an experimental simulation of our information sharing model. In our laboratory, five participants accumulated a personal repository of e-mails and web pages from which they built their own concept-base. Then we estimated the user profiles according to personalized concept-bases and sets of documents which others evaluated. We simulated

A learning-based agent for home neurorehabilitation.

PubMed

Lydakis, Andreas; Meng, Yuanliang; Munroe, Christopher; Wu, Yi-Ning; Begum, Momotaz

2017-07-01

This paper presents the iterative development of an artificially intelligent system to promote home-based neurorehabilitation. Although proper, structured practice of rehabilitation exercises at home is the key to successful recovery of motor functions, there is no home-program out there which can monitor a patient's exercise-related activities and provide corrective feedback in real time. To this end, we designed a Learning from Demonstration (LfD) based home-rehabilitation framework that combines advanced robot learning algorithms with commercially available wearable technologies. The proposed system uses exercise-related motion information and electromyography signals (EMG) of a patient to train a Markov Decision Process (MDP). The trained MDP model can enable an agent to serve as a coach for a patient. On a system level, this is the first initiative, to the best of our knowledge, to employ LfD in an health-care application to enable lay users to program an intelligent system. From a rehabilitation research perspective, this is a completely novel initiative to employ machine learning to provide interactive corrective feedback to a patient in home settings.

Hybrid employment recommendation algorithm based on Spark

NASA Astrophysics Data System (ADS)

Li, Zuoquan; Lin, Yubei; Zhang, Xingming

2017-08-01

Aiming at the real-time application of collaborative filtering employment recommendation algorithm (CF), a clustering collaborative filtering recommendation algorithm (CCF) is developed, which applies hierarchical clustering to CF and narrows the query range of neighbour items. In addition, to solve the cold-start problem of content-based recommendation algorithm (CB), a content-based algorithm with users’ information (CBUI) is introduced for job recommendation. Furthermore, a hybrid recommendation algorithm (HRA) which combines CCF and CBUI algorithms is proposed, and implemented on Spark platform. The experimental results show that HRA can overcome the problems of cold start and data sparsity, and achieve good recommendation accuracy and scalability for employment recommendation.

Protection of Location Privacy Based on Distributed Collaborative Recommendations

PubMed Central

Wang, Peng; Yang, Jing; Zhang, Jian-Pei

2016-01-01

In the existing centralized location services system structure, the server is easily attracted and be the communication bottleneck. It caused the disclosure of users’ location. For this, we presented a new distributed collaborative recommendation strategy that is based on the distributed system. In this strategy, each node establishes profiles of their own location information. When requests for location services appear, the user can obtain the corresponding location services according to the recommendation of the neighboring users’ location information profiles. If no suitable recommended location service results are obtained, then the user can send a service request to the server according to the construction of a k-anonymous data set with a centroid position of the neighbors. In this strategy, we designed a new model of distributed collaborative recommendation location service based on the users’ location information profiles and used generalization and encryption to ensure the safety of the user’s location information privacy. Finally, we used the real location data set to make theoretical and experimental analysis. And the results show that the strategy proposed in this paper is capable of reducing the frequency of access to the location server, providing better location services and protecting better the user’s location privacy. PMID:27649308

Protection of Location Privacy Based on Distributed Collaborative Recommendations.

PubMed

Wang, Peng; Yang, Jing; Zhang, Jian-Pei

2016-01-01

In the existing centralized location services system structure, the server is easily attracted and be the communication bottleneck. It caused the disclosure of users' location. For this, we presented a new distributed collaborative recommendation strategy that is based on the distributed system. In this strategy, each node establishes profiles of their own location information. When requests for location services appear, the user can obtain the corresponding location services according to the recommendation of the neighboring users' location information profiles. If no suitable recommended location service results are obtained, then the user can send a service request to the server according to the construction of a k-anonymous data set with a centroid position of the neighbors. In this strategy, we designed a new model of distributed collaborative recommendation location service based on the users' location information profiles and used generalization and encryption to ensure the safety of the user's location information privacy. Finally, we used the real location data set to make theoretical and experimental analysis. And the results show that the strategy proposed in this paper is capable of reducing the frequency of access to the location server, providing better location services and protecting better the user's location privacy.

Salivary dysfunction associated with systemic diseases: systematic review and clinical management recommendations.

PubMed

von Bültzingslöwen, Inger; Sollecito, Thomas P; Fox, Philip C; Daniels, Troy; Jonsson, Roland; Lockhart, Peter B; Wray, David; Brennan, Michael T; Carrozzo, Marco; Gandera, Beatrice; Fujibayashi, Takashi; Navazesh, Mahvash; Rhodus, Nelson L; Schiødt, Morten

2007-03-01

The objective of this study was to identify systemic diseases associated with hyposalivation and xerostomia and develop evidence-based management recommendations for hyposalivation/xerostomia. Literature searches covered the English language medical literature from 1966 to 2005. An evidence-based review process was applied to management studies published from 2002 to 2005. Several systemic diseases were identified. From studies published 2002 to 2005, 15 were identified as high-quality studies and were used to support management recommendations: pilocarpine and cevimeline are recommended for treating hyposalivation and xerostomia in primary and secondary Sjögren's syndrome (SS). IFN-alpha lozenges may enhance saliva flow in primary SS patients. Anti-TNF-alpha agents, such as infliximab or etanercept, are not recommended to treat hyposalivation in SS. Dehydroepiandrosterone is not recommended to relieve hyposalivation or xerostomia in primary SS. There was not enough evidence to support any recommendations for the use of local stimulants, lubricants, and protectants for hyposalivation/xerostomia. However, professional judgment and patient preferences may support the use of a specific product for an individual patient. These evidence-based management recommendations should guide the clinician's management decisions for patients with salivary dysfunction related to systemic disease. Future treatment strategies may include new formulations of existing drugs, e.g., local application of pilocarpine. Recent discoveries on gene expression and a better understanding of the etiopathogenesis of SS may open new treatment options in the future.

The SAPO Campus Recommender System: A Study about Students' and Teachers' Opinions

ERIC Educational Resources Information Center

Pedro, Luís; Santos, Carlos; Almeida, Sara Filipa; Ramos, Fernando; Moreira, António; Almeida, Margarida; Antunes, Maria João

2014-01-01

This paper aims to assess the relevance and usefulness of the SAPO Campus recommender system, through the analysis of students' and teachers' opinions. Recommender systems, assuming a "technology-driven" approach, have been designed with the primary goal of predicting user interests based on the implicit analysis of their actions and…

Music recommendation system for biofied building considering multiple residents

NASA Astrophysics Data System (ADS)

Ito, Takahiro; Mita, Akira

2012-04-01

This research presents a music recommendation system based on multiple users' communication excitement and productivity. Evaluation is conducted on following two points. 1, Does songA recommended by the system improve the situation of dropped down communication excitement? 2, Does songB recommended by the system improve the situation of dropped down and productivity of collaborative work? The objective of this system is to recommend songs which shall improve the situation of dropped down communication excitement and productivity. Songs are characterized according to three aspects; familiarity, relaxing and BPM(Beat Per Minutes). Communication excitement is calculated from speech data obtained by an audio sensor. Productivity of collaborative brainstorming is manually calculated by the number of time-series key words during mind mapping. First experiment was music impression experiment to 118 students. Based on 1, average points of familiarity, relaxing and BPM 2, cronbach alpha factor, songA(high familiarity, high relaxing and high BPM song) and songB(high familiarity, high relaxing and low BPM) are selected. Exploratory experiment defined dropped down communication excitement and dropped down and productivity of collaborative work. Final experiment was conducted to 32 first meeting students divided into 8 groups. First 4 groups had mind mapping 1 while listening to songA, then had mind mapping 2 while listening songB. Following 4 groups had mind mapping 1 while listening to songB, then had mind mapping 2 while listening songA. Fianl experiment shows two results. Firstly, ratio of communication excitement between music listening section and whole brain storming is 1.27. Secondly, this system increases 69% of average productivity.

Empirical comparison of local structural similarity indices for collaborative-filtering-based recommender systems

NASA Astrophysics Data System (ADS)

Zhang, Qian-Ming; Shang, Ming-Sheng; Zeng, Wei; Chen, Yong; Lü, Linyuan

2010-08-01

Collaborative filtering is one of the most successful recommendation techniques, which can effectively predict the possible future likes of users based on their past preferences. The key problem of this method is how to define the similarity between users. A standard approach is using the correlation between the ratings that two users give to a set of objects, such as Cosine index and Pearson correlation coefficient. However, the costs of computing this kind of indices are relatively high, and thus it is impossible to be applied in the huge-size systems. To solve this problem, in this paper, we introduce six local-structure-based similarity indices and compare their performances with the above two benchmark indices. Experimental results on two data sets demonstrate that the structure-based similarity indices overall outperform the Pearson correlation coefficient. When the data is dense, the structure-based indices can perform competitively good as Cosine index, while with lower computational complexity. Furthermore, when the data is sparse, the structure-based indices give even better results than Cosine index.

Collective-Intelligence Recommender Systems: Advancing Computer Tailoring for Health Behavior Change Into the 21st Century.

PubMed

Sadasivam, Rajani Shankar; Cutrona, Sarah L; Kinney, Rebecca L; Marlin, Benjamin M; Mazor, Kathleen M; Lemon, Stephenie C; Houston, Thomas K

2016-03-07

What is the next frontier for computer-tailored health communication (CTHC) research? In current CTHC systems, study designers who have expertise in behavioral theory and mapping theory into CTHC systems select the variables and develop the rules that specify how the content should be tailored, based on their knowledge of the targeted population, the literature, and health behavior theories. In collective-intelligence recommender systems (hereafter recommender systems) used by Web 2.0 companies (eg, Netflix and Amazon), machine learning algorithms combine user profiles and continuous feedback ratings of content (from themselves and other users) to empirically tailor content. Augmenting current theory-based CTHC with empirical recommender systems could be evaluated as the next frontier for CTHC. The objective of our study was to uncover barriers and challenges to using recommender systems in health promotion. We conducted a focused literature review, interviewed subject experts (n=8), and synthesized the results. We describe (1) limitations of current CTHC systems, (2) advantages of incorporating recommender systems to move CTHC forward, and (3) challenges to incorporating recommender systems into CTHC. Based on the evidence presented, we propose a future research agenda for CTHC systems. We promote discussion of ways to move CTHC into the 21st century by incorporation of recommender systems.

Collective-Intelligence Recommender Systems: Advancing Computer Tailoring for Health Behavior Change Into the 21st Century

PubMed Central

Cutrona, Sarah L; Kinney, Rebecca L; Marlin, Benjamin M; Mazor, Kathleen M; Lemon, Stephenie C; Houston, Thomas K

2016-01-01

Background What is the next frontier for computer-tailored health communication (CTHC) research? In current CTHC systems, study designers who have expertise in behavioral theory and mapping theory into CTHC systems select the variables and develop the rules that specify how the content should be tailored, based on their knowledge of the targeted population, the literature, and health behavior theories. In collective-intelligence recommender systems (hereafter recommender systems) used by Web 2.0 companies (eg, Netflix and Amazon), machine learning algorithms combine user profiles and continuous feedback ratings of content (from themselves and other users) to empirically tailor content. Augmenting current theory-based CTHC with empirical recommender systems could be evaluated as the next frontier for CTHC. Objective The objective of our study was to uncover barriers and challenges to using recommender systems in health promotion. Methods We conducted a focused literature review, interviewed subject experts (n=8), and synthesized the results. Results We describe (1) limitations of current CTHC systems, (2) advantages of incorporating recommender systems to move CTHC forward, and (3) challenges to incorporating recommender systems into CTHC. Based on the evidence presented, we propose a future research agenda for CTHC systems. Conclusions We promote discussion of ways to move CTHC into the 21st century by incorporation of recommender systems. PMID:26952574

Do recommender systems benefit users? a modeling approach

NASA Astrophysics Data System (ADS)

Yeung, Chi Ho

2016-04-01

Recommender systems are present in many web applications to guide purchase choices. They increase sales and benefit sellers, but whether they benefit customers by providing relevant products remains less explored. While in many cases the recommended products are relevant to users, in other cases customers may be tempted to purchase the products only because they are recommended. Here we introduce a model to examine the benefit of recommender systems for users, and find that recommendations from the system can be equivalent to random draws if one always follows the recommendations and seldom purchases according to his or her own preference. Nevertheless, with sufficient information about user preferences, recommendations become accurate and an abrupt transition to this accurate regime is observed for some of the studied algorithms. On the other hand, we find that high estimated accuracy indicated by common accuracy metrics is not necessarily equivalent to high real accuracy in matching users with products. This disagreement between estimated and real accuracy serves as an alarm for operators and researchers who evaluate recommender systems merely with accuracy metrics. We tested our model with a real dataset and observed similar behaviors. Finally, a recommendation approach with improved accuracy is suggested. These results imply that recommender systems can benefit users, but the more frequently a user purchases the recommended products, the less relevant the recommended products are in matching user taste.

Personalized recommendation based on unbiased consistence

NASA Astrophysics Data System (ADS)

Zhu, Xuzhen; Tian, Hui; Zhang, Ping; Hu, Zheng; Zhou, Tao

2015-08-01

Recently, in physical dynamics, mass-diffusion-based recommendation algorithms on bipartite network provide an efficient solution by automatically pushing possible relevant items to users according to their past preferences. However, traditional mass-diffusion-based algorithms just focus on unidirectional mass diffusion from objects having been collected to those which should be recommended, resulting in a biased causal similarity estimation and not-so-good performance. In this letter, we argue that in many cases, a user's interests are stable, and thus bidirectional mass diffusion abilities, no matter originated from objects having been collected or from those which should be recommended, should be consistently powerful, showing unbiased consistence. We further propose a consistence-based mass diffusion algorithm via bidirectional diffusion against biased causality, outperforming the state-of-the-art recommendation algorithms in disparate real data sets, including Netflix, MovieLens, Amazon and Rate Your Music.

Towards an agent based traffic regulation and recommendation system for the on-road air quality control.

PubMed

Sadiq, Abderrahmane; El Fazziki, Abdelaziz; Ouarzazi, Jamal; Sadgal, Mohamed

2016-01-01

This paper presents an integrated and adaptive problem-solving approach to control the on-road air quality by modeling the road infrastructure, managing traffic based on pollution level and generating recommendations for road users. The aim is to reduce vehicle emissions in the most polluted road segments and optimizing the pollution levels. For this we propose the use of historical and real time pollution records and contextual data to calculate the air quality index on road networks and generate recommendations for reassigning traffic flow in order to improve the on-road air quality. The resulting air quality indexes are used in the system's traffic network generation, which the cartography is represented by a weighted graph. The weights evolve according to the pollution indexes and path properties and the graph is therefore dynamic. Furthermore, the systems use the available pollution data and meteorological records in order to predict the on-road pollutant levels by using an artificial neural network based prediction model. The proposed approach combines the benefits of multi-agent systems, Big data technology, machine learning tools and the available data sources. For the shortest path searching in the road network, we use the Dijkstra algorithm over Hadoop MapReduce framework. The use Hadoop framework in the data retrieve and analysis process has significantly improved the performance of the proposed system. Also, the agent technology allowed proposing a suitable solution in terms of robustness and agility.

Evidence-based recommendations to improve reproductive healthcare for incarcerated women.

PubMed

Knittel, Andrea; Ti, Angeline; Schear, Sarah; Comfort, Megan

2017-09-11

Purpose The purpose of this paper is to describe standards for evidence-based reproductive healthcare for incarcerated women. Design/methodology/approach The literature on reproductive healthcare in the US criminal justice system and recommendations from professional organizations were reviewed and critical areas of concern were identified. Within these areas, studies and expert opinion were synthesized and policy recommendations were formulated through an iterative process of group discussion and document revision. This brief specifically addresses women's incarceration in the USA, but the recommendations are grounded in a human rights framework with global relevance. Findings Women who are incarcerated have health needs that are distinct from those of men, and there is a clear need for gender-responsive reproductive healthcare within the criminal justice system. This brief identifies five core domains of reproductive healthcare: routine screening, menstruation-related concerns, prenatal and postpartum care, contraception and abortion, and sexually transmitted infections. The recommendations emphasize the continuity between the criminal justice system and the community, as well as the dignity and self-determination of incarcerated women. Originality/value This brief provides a unique synthesis of the available evidence with concrete recommendations for improving the reproductive healthcare for incarcerated women.

Constructing compact and effective graphs for recommender systems via node and edge aggregations

DOE PAGES

Lee, Sangkeun; Kahng, Minsuk; Lee, Sang-goo

2014-12-10

Exploiting graphs for recommender systems has great potential to flexibly incorporate heterogeneous information for producing better recommendation results. As our baseline approach, we first introduce a naive graph-based recommendation method, which operates with a heterogeneous log-metadata graph constructed from user log and content metadata databases. Although the na ve graph-based recommendation method is simple, it allows us to take advantages of heterogeneous information and shows promising flexibility and recommendation accuracy. However, it often leads to extensive processing time due to the sheer size of the graphs constructed from entire user log and content metadata databases. In this paper, we proposemore » node and edge aggregation approaches to constructing compact and e ective graphs called Factor-Item bipartite graphs by aggregating nodes and edges of a log-metadata graph. Furthermore, experimental results using real world datasets indicate that our approach can significantly reduce the size of graphs exploited for recommender systems without sacrificing the recommendation quality.« less

Towards Information Enrichment through Recommendation Sharing

NASA Astrophysics Data System (ADS)

Weng, Li-Tung; Xu, Yue; Li, Yuefeng; Nayak, Richi

Nowadays most existing recommender systems operate in a single organisational basis, i.e. a recommender system recommends items to customers of one organisation based on the organisation's datasets only. Very often the datasets of a single organisation do not have sufficient resources to be used to generate quality recommendations. Therefore, it would be beneficial if recommender systems of different organisations with similar nature can cooperate together to share their resources and recommendations. In this chapter, we present an Ecommerce-oriented Distributed Recommender System (EDRS) that consists of multiple recommender systems from different organisations. By sharing resources and recommendations with each other, these recommenders in the distributed recommendation system can provide better recommendation service to their users. As for most of the distributed systems, peer selection is often an important aspect. This chapter also presents a recommender selection technique for the proposed EDRS, and it selects and profiles recommenders based on their stability, average performance and selection frequency. Based on our experiments, it is shown that recommenders' recommendation quality can be effectively improved by adopting the proposed EDRS and the associated peer selection technique.

Career Goal-Based E-Learning Recommendation Using Enhanced Collaborative Filtering and PrefixSpan

ERIC Educational Resources Information Center

Ma, Xueying; Ye, Lu

2018-01-01

This article describes how e-learning recommender systems nowadays have applied different kinds of techniques to recommend personalized learning content for users based on their preference, goals, interests and background information. However, the cold-start problem which exists in traditional recommendation algorithms are still left over in…

[GRADE system: classification of quality of evidence and strength of recommendation].

PubMed

Aguayo-Albasini, José Luis; Flores-Pastor, Benito; Soria-Aledo, Víctor

2014-02-01

The acquisition and classification of scientific evidence, and subsequent formulation of recommendations constitute the basis for the development of clinical practice guidelines. There are several systems for the classification of evidence and strength of recommendations; the most commonly used nowadays is the Grading of Recommendations, Assessment, Development and Evaluation system (GRADE). The GRADE system initially classifies the evidence into high or low, coming from experimental or observational studies; subsequently and following a series of considerations, the evidence is classified into high, moderate, low or very low. The strength of recommendations is based not only on the quality of the evidence, but also on a series of factors such as the risk/benefit balance, values and preferences of the patients and professionals, and the use of resources or costs. Copyright © 2013 AEC. Published by Elsevier Espana. All rights reserved.

Modeling mutual feedback between users and recommender systems

NASA Astrophysics Data System (ADS)

Zeng, An; Yeung, Chi Ho; Medo, Matúš; Zhang, Yi-Cheng

2015-07-01

Recommender systems daily influence our decisions on the Internet. While considerable attention has been given to issues such as recommendation accuracy and user privacy, the long-term mutual feedback between a recommender system and the decisions of its users has been neglected so far. We propose here a model of network evolution which allows us to study the complex dynamics induced by this feedback, including the hysteresis effect which is typical for systems with non-linear dynamics. Despite the popular belief that recommendation helps users to discover new things, we find that the long-term use of recommendation can contribute to the rise of extremely popular items and thus ultimately narrow the user choice. These results are supported by measurements of the time evolution of item popularity inequality in real systems. We show that this adverse effect of recommendation can be tamed by sacrificing part of short-term recommendation accuracy.

Scientific and educational recommender systems

NASA Astrophysics Data System (ADS)

Guseva, A. I.; Kireev, V. S.; Bochkarev, P. V.; Kuznetsov, I. A.; Philippov, S. A.

2017-01-01

This article discusses the questions associated with the use of reference systems in the preparation of graduates in physical function. The objective of this research is creation of model of recommender system user from the sphere of science and education. The detailed review of current scientific and social network for scientists and the problem of constructing recommender systems in this area. The result of this study is to research user information model systems. The model is presented in two versions: the full one - in the form of a semantic network, and short - in a relational form. The relational model is the projection in the form of semantic network, taking into account the restrictions on the amount of bonds that characterize the number of information items (research results), which interact with the system user.

Rural applications of advanced traveler information systems : recommended actions

DOT National Transportation Integrated Search

1997-07-01

The Recommended Action Plan is one in a series of interim documents for the Rural Applications of Advanced Traveler Information Systems (ATIS) project. Based on the investigation of user needs, a technology review, and concept development and assessm...

Data You May Like: A Recommender System for Research Data Discovery

NASA Astrophysics Data System (ADS)

Devaraju, A.; Davy, R.; Hogan, D.

2016-12-01

Various data portals been developed to facilitate access to research datasets from different sources. For example, the Data Publisher for Earth & Environmental Science (PANGAEA), the Registry of Research Data Repositories (re3data.org), and the National Geoscience Data Centre (NGDC). Due to data quantity and heterogeneity, finding relevant datasets on these portals may be difficult and tedious. Keyword searches based on specific metadata elements or multi-key indexes may return irrelevant results. Faceted searches may be unsatisfactory and time consuming, especially when facet values are exhaustive. We need a much more intelligent way to complement existing searching mechanisms in order to enhance user experiences of the data portals. We developed a recommender system that helps users to find the most relevant research datasets on the CSIRO's Data Access Portal (DAP). The system is based on content-based filtering. We computed the similarity of datasets based on data attributes (e.g., descriptions, fields of research, location, contributors, and provenance) and inference from transaction logs (e.g., the relations among datasets and between queries and datasets). We improved the recommendation quality by assigning weights to data similarities. The weight values are drawn from a survey involving data users. The recommender results for a given dataset are accessible programmatically via a web service. Taking both data attributes and user actions into account, the recommender system will make it easier for researchers to find and reuse data offered through the data portal.

SemCiR: A Citation Recommendation System Based on a Novel Semantic Distance Measure

ERIC Educational Resources Information Center

Zarrinkalam, Fattane; Kahani, Mohsen

2013-01-01

Purpose: The purpose of this paper is to propose a novel citation recommendation system that inputs a text and recommends publications that should be cited by it. Its goal is to help researchers in finding related works. Further, this paper seeks to explore the effect of using relational features in addition to textual features on the quality of…

Low-Cost, Rugged High-Vacuum System

NASA Technical Reports Server (NTRS)

Sorensen, Paul; Kline-Schoder, Robert

2012-01-01

A need exists for miniaturized, rugged, low-cost high-vacuum systems. Recent advances in sensor technology have led to the development of very small mass spectrometer detectors as well as other analytical instruments such as scanning electron microscopes. However, the vacuum systems to support these sensors remain large, heavy, and power-hungry. To meet this need, a miniaturized vacuum system was developed based on a very small, rugged, and inexpensive-to-manufacture molecular drag pump (MDP). The MDP is enabled by a miniature, very-high-speed (200,000 rpm), rugged, low-power, brushless DC motor optimized for wide temperature operation and long life. The key advantages of the pump are reduced cost and improved ruggedness compared to other mechanical hig-hvacuum pumps. The machining of the rotor and stators is very simple compared to that necessary to fabricate rotor and stator blades for other pump designs. Also, the symmetry of the rotor is such that dynamic balancing of the rotor will likely not be necessary. Finally, the number of parts in the unit is cut by nearly a factor of three over competing designs. The new pump forms the heart of a complete vacuum system optimized to support analytical instruments in terrestrial applications and on spacecraft and planetary landers. The MDP achieves high vacuum coupled to a ruggedized diaphragm rough pump. Instead of the relatively complicated rotor and stator blades used in turbomolecular pumps, the rotor in the MDP consists of a simple, smooth cylinder of aluminum. This will turn at approximately 200,000 rpm inside an outer stator housing. The pump stator comprises a cylindrical aluminum housing with one or more specially designed grooves that serve as flow channels. To minimize the length of the pump, the gas is forced down the flow channels of the outer stator to the base of the pump. The gas is then turned and pulled toward the top through a second set of channels cut into an inner stator housing that surrounds the

Municipal water-based heat pump heating and/or cooling systems: Findings and recommendations. Final report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bloomquist, R.G.; Wegman, S.

1998-04-01

The purpose of the present work was to determine if existing heat pump systems based on municipal water systems meet existing water quality standards, to analyze water that has passed through a heat pump or heat exchanger to determine if corrosion products can be detected, to determine residual chlorine levels in municipal waters on the inlet as well as the outlet side of such installations, to analyses for bacterial contaminants and/or regrowth due to the presence of a heat pump or heat exchanger, to develop and suggest criteria for system design and construction, to provide recommendations and specifications for materialmore » and fluid selection, and to develop model rules and regulations for the installation, operation, and monitoring of new and existing systems. In addition, the Washington State University (WSU) has evaluated availability of computer models that would allow for water system mapping, water quality modeling and system operation.« less

Evidence-based cancer prevention recommendations for Japanese.

PubMed

Sasazuki, S; Inoue, M; Shimazu, T; Wakai, K; Naito, M; Nagata, C; Tanaka, K; Tsuji, I; Sugawara, Y; Mizoue, T; Matsuo, K; Ito, H; Tamakoshi, A; Sawada, N; Nakayama, T; Kitamura, Y; Sadakane, A; Tsugane, S

2018-06-01

A comprehensive evidence-based cancer prevention recommendation for Japanese was developed. We evaluated the magnitude of the associations of lifestyle factors and infection with cancer through a systematic review of the literature, meta-analysis of published data, and pooled analysis of cohort studies in Japan. Then, we judged the strength of evidence based on the consistency of the associations between exposure and cancer and biological plausibility. Important factors were extracted and summarized as an evidence-based, current cancer prevention recommendation: 'Cancer Prevention Recommendation for Japanese'. The recommendation addresses six important domains related to exposure and cancer, including smoking, alcohol drinking, diet, physical activity, body weight and infection. The next step should focus on the development of effective behavior modification programs and their implementation and dissemination.

Usability Guidelines for Product Recommenders Based on Example Critiquing Research

NASA Astrophysics Data System (ADS)

Pu, Pearl; Faltings, Boi; Chen, Li; Zhang, Jiyong; Viappiani, Paolo

Over the past decade, our group has developed a suite of decision tools based on example critiquing to help users find their preferred products in e-commerce environments. In this chapter, we survey important usability research work relative to example critiquing and summarize the major results by deriving a set of usability guidelines. Our survey is focused on three key interaction activities between the user and the system: the initial preference elicitation process, the preference revision process, and the presentation of the systems recommendation results. To provide a basis for the derivation of the guidelines, we developed a multi-objective framework of three interacting criteria: accuracy, confidence, and effort (ACE). We use this framework to analyze our past work and provide a specific context for each guideline: when the system should maximize its ability to increase users' decision accuracy, when to increase user confidence, and when to minimize the interaction effort for the users. Due to the general nature of this multi-criteria model, the set of guidelines that we propose can be used to ease the usability engineering process of other recommender systems, especially those used in e-commerce environments. The ACE framework presented here is also the first in the field to evaluate the performance of preference-based recommenders from a user-centric point of view.

A Recommender System for an IPTV Service Provider: a Real Large-Scale Production Environment

NASA Astrophysics Data System (ADS)

Bambini, Riccardo; Cremonesi, Paolo; Turrin, Roberto

In this chapter we describe the integration of a recommender system into the production environment of Fastweb, one of the largest European IP Television (IPTV) providers. The recommender system implements both collaborative and content-based techniques, suitable tailored to the specific requirements of an IPTV architecture, such as the limited screen definition, the reduced navigation capabilities, and the strict time constraints. The algorithms are extensively analyzed by means of off-line and on-line tests, showing the effectiveness of the recommender systems: up to 30% of the recommendations are followed by a purchase, with an estimated lift factor (increase in sales) of 15%.

Long-term effects of user preference-oriented recommendation method on the evolution of online system

NASA Astrophysics Data System (ADS)

Shi, Xiaoyu; Shang, Ming-Sheng; Luo, Xin; Khushnood, Abbas; Li, Jian

2017-02-01

As the explosion growth of Internet economy, recommender system has become an important technology to solve the problem of information overload. However, recommenders are not one-size-fits-all, different recommenders have different virtues, making them be suitable for different users. In this paper, we propose a novel personalized recommender based on user preferences, which allows multiple recommenders to exist in E-commerce system simultaneously. We find that output of a recommender to each user is quite different when using different recommenders, the recommendation accuracy can be significantly improved if each user is assigned with his/her optimal personalized recommender. Furthermore, different from previous works focusing on short-term effects on recommender, we also evaluate the long-term effect of the proposed method by modeling the evolution of mutual feedback between user and online system. Finally, compared with single recommender running on the online system, the proposed method can improve the accuracy of recommendation significantly and get better trade-offs between short- and long-term performances of recommendation.

A Data Management System Integrating Web-based Training and Randomized Trials: Requirements, Experiences and Recommendations.

PubMed

Muroff, Jordana; Amodeo, Maryann; Larson, Mary Jo; Carey, Margaret; Loftin, Ralph D

2011-01-01

This article describes a data management system (DMS) developed to support a large-scale randomized study of an innovative web-course that was designed to improve substance abuse counselors' knowledge and skills in applying a substance abuse treatment method (i.e., cognitive behavioral therapy; CBT). The randomized trial compared the performance of web-course-trained participants (intervention group) and printed-manual-trained participants (comparison group) to determine the effectiveness of the web-course in teaching CBT skills. A single DMS was needed to support all aspects of the study: web-course delivery and management, as well as randomized trial management. The authors briefly reviewed several other systems that were described as built either to handle randomized trials or to deliver and evaluate web-based training. However it was clear that these systems fell short of meeting our needs for simultaneous, coordinated management of the web-course and the randomized trial. New England Research Institute's (NERI) proprietary Advanced Data Entry and Protocol Tracking (ADEPT) system was coupled with the web-programmed course and customized for our purposes. This article highlights the requirements for a DMS that operates at the intersection of web-based course management systems and randomized clinical trial systems, and the extent to which the coupled, customized ADEPT satisfied those requirements. Recommendations are included for institutions and individuals considering conducting randomized trials and web-based training programs, and seeking a DMS that can meet similar requirements.

Web-based counseling for problem gambling: exploring motivations and recommendations.

PubMed

Rodda, Simone; Lubman, Dan I; Dowling, Nicki A; Bough, Anna; Jackson, Alun C

2013-05-24

For highly stigmatized disorders, such as problem gambling, Web-based counseling has the potential to address common barriers to treatment, including issues of shame and stigma. Despite the exponential growth in the uptake of immediate synchronous Web-based counseling (ie, provided without appointment), little is known about why people choose this service over other modes of treatment. The aim of the current study was to determine motivations for choosing and recommending Web-based counseling over telephone or face-to-face services. The study involved 233 Australian participants who had completed an online counseling session for problem gambling on the Gambling Help Online website between November 2010 and February 2012. Participants were all classified as problem gamblers, with a greater proportion of males (57.4%) and 60.4% younger than 40 years of age. Participants completed open-ended questions about their reasons for choosing online counseling over other modes (ie, face-to-face and telephone), as well as reasons for recommending the service to others. A content analysis revealed 4 themes related to confidentiality/anonymity (reported by 27.0%), convenience/accessibility (50.9%), service system access (34.2%), and a preference for the therapeutic medium (26.6%). Few participants reported helpful professional support as a reason for accessing counseling online, but 43.2% of participants stated that this was a reason for recommending the service. Those older than 40 years were more likely than younger people in the sample to use Web-based counseling as an entry point into the service system (P=.045), whereas those engaged in nonstrategic gambling (eg, machine gambling) were more likely to access online counseling as an entry into the service system than those engaged in strategic gambling (ie, cards, sports; P=.01). Participants older than 40 years were more likely to recommend the service because of its potential for confidentiality and anonymity (P=.04

Web-Based Counseling for Problem Gambling: Exploring Motivations and Recommendations

PubMed Central

Lubman, Dan I; Dowling, Nicki A; Bough, Anna; Jackson, Alun C

2013-01-01

Background For highly stigmatized disorders, such as problem gambling, Web-based counseling has the potential to address common barriers to treatment, including issues of shame and stigma. Despite the exponential growth in the uptake of immediate synchronous Web-based counseling (ie, provided without appointment), little is known about why people choose this service over other modes of treatment. Objective The aim of the current study was to determine motivations for choosing and recommending Web-based counseling over telephone or face-to-face services. Methods The study involved 233 Australian participants who had completed an online counseling session for problem gambling on the Gambling Help Online website between November 2010 and February 2012. Participants were all classified as problem gamblers, with a greater proportion of males (57.4%) and 60.4% younger than 40 years of age. Participants completed open-ended questions about their reasons for choosing online counseling over other modes (ie, face-to-face and telephone), as well as reasons for recommending the service to others. Results A content analysis revealed 4 themes related to confidentiality/anonymity (reported by 27.0%), convenience/accessibility (50.9%), service system access (34.2%), and a preference for the therapeutic medium (26.6%). Few participants reported helpful professional support as a reason for accessing counseling online, but 43.2% of participants stated that this was a reason for recommending the service. Those older than 40 years were more likely than younger people in the sample to use Web-based counseling as an entry point into the service system (P=.045), whereas those engaged in nonstrategic gambling (eg, machine gambling) were more likely to access online counseling as an entry into the service system than those engaged in strategic gambling (ie, cards, sports; P=.01). Participants older than 40 years were more likely to recommend the service

Opinion-enhanced collaborative filtering for recommender systems through sentiment analysis

NASA Astrophysics Data System (ADS)

Wang, Wei; Wang, Hongwei

2015-10-01

The motivation of collaborative filtering (CF) comes from the idea that people often get the best recommendations from someone with similar tastes. With the growing popularity of opinion-rich resources such as online reviews, new opportunities arise as we can identify the preferences from user opinions. The main idea of our approach is to elicit user opinions from online reviews, and map such opinions into preferences that can be understood by CF-based recommender systems. We divide recommender systems into two types depending on the number of product category recommended: the multiple-category recommendation and the single-category recommendation. For the former, sentiment polarity in coarse-grained manner is identified while for the latter fine-grained sentiment analysis is conducted for each product aspect. If the evaluation frequency for an aspect by a user is greater than the average frequency by all users, it indicates that the user is more concerned with that aspect. If a user's rating for an aspect is lower than the average rating by all users, he or she is much pickier than others on that aspect. Through sentiment analysis, we then build an opinion-enhanced user preference model, where the higher the similarity between user opinions the more consistent preferences between users are. Experiment results show that the proposed CF algorithm outperforms baseline methods for product recommendation in terms of accuracy and recall.

The Power of Ground User in Recommender Systems

PubMed Central

Zhou, Yanbo; Lü, Linyuan; Liu, Weiping; Zhang, Jianlin

2013-01-01

Accuracy and diversity are two important aspects to evaluate the performance of recommender systems. Two diffusion-based methods were proposed respectively inspired by the mass diffusion (MD) and heat conduction (HC) processes on networks. It has been pointed out that MD has high recommendation accuracy yet low diversity, while HC succeeds in seeking out novel or niche items but with relatively low accuracy. The accuracy-diversity dilemma is a long-term challenge in recommender systems. To solve this problem, we introduced a background temperature by adding a ground user who connects to all the items in the user-item bipartite network. Performing the HC algorithm on the network with ground user (GHC), it showed that the accuracy can be largely improved while keeping the diversity. Furthermore, we proposed a weighted form of the ground user (WGHC) by assigning some weights to the newly added links between the ground user and the items. By turning the weight as a free parameter, an optimal value subject to the highest accuracy is obtained. Experimental results on three benchmark data sets showed that the WGHC outperforms the state-of-the-art method MD for both accuracy and diversity. PMID:23936380

Development of sensor-based nitrogen recommendation algorithms for cereal crops

NASA Astrophysics Data System (ADS)

Asebedo, Antonio Ray

Nitrogen (N) management is one of the most recognizable components of farming both within and outside the world of agriculture. Interest over the past decade has greatly increased in improving N management systems in corn (Zea mays) and winter wheat (Triticum aestivum ) to have high NUE, high yield, and be environmentally sustainable. Nine winter wheat experiments were conducted across seven locations from 2011 through 2013. The objectives of this study were to evaluate the impacts of fall-winter, Feekes 4, Feekes 7, and Feekes 9 N applications on winter wheat grain yield, grain protein, and total grain N uptake. Nitrogen treatments were applied as single or split applications in the fall-winter, and top-dressed in the spring at Feekes 4, Feekes 7, and Feekes 9 with applied N rates ranging from 0 to 134 kg ha-1. Results indicate that Feekes 7 and 9 N applications provide more optimal combinations of grain yield, grain protein levels, and fertilizer N recovered in the grain when compared to comparable rates of N applied in the fall-winter or at Feekes 4. Winter wheat N management studies from 2006 through 2013 were utilized to develop sensor-based N recommendation algorithms for winter wheat in Kansas. Algorithm RosieKat v.2.6 was designed for multiple N application strategies and utilized N reference strips for establishing N response potential. Algorithm NRS v1.5 addressed single top-dress N applications and does not require a N reference strip. In 2013, field validations of both algorithms were conducted at eight locations across Kansas. Results show algorithm RK v2.6 consistently provided highly efficient N recommendations for improving NUE, while achieving high grain yield and grain protein. Without the use of the N reference strip, NRS v1.5 performed statistically equal to the KSU soil test N recommendation in regards to grain yield but with lower applied N rates. Six corn N fertigation experiments were conducted at KSU irrigated experiment fields from 2012

Effect of recent popularity on heat-conduction based recommendation models

NASA Astrophysics Data System (ADS)

Li, Wen-Jun; Dong, Qiang; Shi, Yang-Bo; Fu, Yan; He, Jia-Lin

2017-05-01

Accuracy and diversity are two important measures in evaluating the performance of recommender systems. It has been demonstrated that the recommendation model inspired by the heat conduction process has high diversity yet low accuracy. Many variants have been introduced to improve the accuracy while keeping high diversity, most of which regard the current node-degree of an item as its popularity. However in this way, a few outdated items of large degree may be recommended to an enormous number of users. In this paper, we take the recent popularity (recently increased item degrees) into account in the heat-conduction based methods, and propose accordingly the improved recommendation models. Experimental results on two benchmark data sets show that the accuracy can be largely improved while keeping the high diversity compared with the original models.

A scalable and practical one-pass clustering algorithm for recommender system

NASA Astrophysics Data System (ADS)

Khalid, Asra; Ghazanfar, Mustansar Ali; Azam, Awais; Alahmari, Saad Ali

2015-12-01

KMeans clustering-based recommendation algorithms have been proposed claiming to increase the scalability of recommender systems. One potential drawback of these algorithms is that they perform training offline and hence cannot accommodate the incremental updates with the arrival of new data, making them unsuitable for the dynamic environments. From this line of research, a new clustering algorithm called One-Pass is proposed, which is a simple, fast, and accurate. We show empirically that the proposed algorithm outperforms K-Means in terms of recommendation and training time while maintaining a good level of accuracy.

Muramyl Dipeptide-Based Postbiotics Mitigate Obesity-Induced Insulin Resistance via IRF4.

PubMed

Cavallari, Joseph F; Fullerton, Morgan D; Duggan, Brittany M; Foley, Kevin P; Denou, Emmanuel; Smith, Brennan K; Desjardins, Eric M; Henriksbo, Brandyn D; Kim, Kalvin J; Tuinema, Brian R; Stearns, Jennifer C; Prescott, David; Rosenstiel, Philip; Coombes, Brian K; Steinberg, Gregory R; Schertzer, Jonathan D

2017-05-02

Intestinal dysbiosis contributes to obesity and insulin resistance, but intervening with antibiotics, prebiotics, or probiotics can be limited by specificity or sustained changes in microbial composition. Postbiotics include bacterial components such as lipopolysaccharides, which have been shown to promote insulin resistance during metabolic endotoxemia. We found that bacterial cell wall-derived muramyl dipeptide (MDP) is an insulin-sensitizing postbiotic that requires NOD2. Injecting MDP lowered adipose inflammation and reduced glucose intolerance in obese mice without causing weight loss or altering the composition of the microbiome. MDP reduced hepatic insulin resistance during obesity and low-level endotoxemia. NOD1-activating muropeptides worsened glucose tolerance. IRF4 distinguished opposing glycemic responses to different types of peptidoglycan and was required for MDP/NOD2-induced insulin sensitization and lower metabolic tissue inflammation during obesity and endotoxemia. IRF4 was dispensable for exacerbated glucose intolerance via NOD1. Mifamurtide, an MDP-based drug with orphan drug status, was an insulin sensitizer at clinically relevant doses in obese mice. Copyright © 2017 Elsevier Inc. All rights reserved.

Design of a real-time and continua-based framework for care guideline recommendations.

PubMed

Lin, Yu-Feng; Shie, Hsin-Han; Yang, Yi-Ching; Tseng, Vincent S

2014-04-16

Telehealth is an important issue in the medical and healthcare domains. Although a number of systems have been developed to meet the demands of emerging telehealth services, the following problems still remain to be addressed: (1) most systems do not monitor/predict the vital signs states so that they are able to send alarms to caregivers in real-time; (2) most systems do not focus on reducing the amount of work that caregivers need to do, and provide patients with remote care; and (3) most systems do not recommend guidelines for caregivers. This study thus proposes a framework for a real-time and Continua-based Care Guideline Recommendation System (Cagurs) which utilizes mobile device platforms to provide caregivers of chronic patients with real-time care guideline recommendations, and that enables vital signs data to be transmitted between different devices automatically, using the Continua standard. Moreover, the proposed system adopts the episode mining approach to monitor/predict anomalous conditions of patients, and then offers related recommended care guidelines to caregivers so that they can offer preventive care in a timely manner.

Design of a Real-Time and Continua-Based Framework for Care Guideline Recommendations

PubMed Central

Lin, Yu-Feng; Shie, Hsin-Han; Yang, Yi-Ching; Tseng, Vincent S.

2014-01-01

Telehealth is an important issue in the medical and healthcare domains. Although a number of systems have been developed to meet the demands of emerging telehealth services, the following problems still remain to be addressed: (1) most systems do not monitor/predict the vital signs states so that they are able to send alarms to caregivers in real-time; (2) most systems do not focus on reducing the amount of work that caregivers need to do, and provide patients with remote care; and (3) most systems do not recommend guidelines for caregivers. This study thus proposes a framework for a real-time and Continua-based Care Guideline Recommendation System (Cagurs) which utilizes mobile device platforms to provide caregivers of chronic patients with real-time care guideline recommendations, and that enables vital signs data to be transmitted between different devices automatically, using the Continua standard. Moreover, the proposed system adopts the episode mining approach to monitor/predict anomalous conditions of patients, and then offers related recommended care guidelines to caregivers so that they can offer preventive care in a timely manner. PMID:24743843

Data Mining Methods for Recommender Systems

NASA Astrophysics Data System (ADS)

Amatriain, Xavier; Jaimes*, Alejandro; Oliver, Nuria; Pujol, Josep M.

In this chapter, we give an overview of the main Data Mining techniques used in the context of Recommender Systems. We first describe common preprocessing methods such as sampling or dimensionality reduction. Next, we review the most important classification techniques, including Bayesian Networks and Support Vector Machines. We describe the k-means clustering algorithm and discuss several alternatives. We also present association rules and related algorithms for an efficient training process. In addition to introducing these techniques, we survey their uses in Recommender Systems and present cases where they have been successfully applied.

Achieving Optimal Privacy in Trust-Aware Social Recommender Systems

NASA Astrophysics Data System (ADS)

Dokoohaki, Nima; Kaleli, Cihan; Polat, Huseyin; Matskin, Mihhail

Collaborative filtering (CF) recommenders are subject to numerous shortcomings such as centralized processing, vulnerability to shilling attacks, and most important of all privacy. To overcome these obstacles, researchers proposed for utilization of interpersonal trust between users, to alleviate many of these crucial shortcomings. Till now, attention has been mainly paid to strong points about trust-aware recommenders such as alleviating profile sparsity or calculation cost efficiency, while least attention has been paid on investigating the notion of privacy surrounding the disclosure of individual ratings and most importantly protection of trust computation across social networks forming the backbone of these systems. To contribute to addressing problem of privacy in trust-aware recommenders, within this paper, first we introduce a framework for enabling privacy-preserving trust-aware recommendation generation. While trust mechanism aims at elevating recommender's accuracy, to preserve privacy, accuracy of the system needs to be decreased. Since within this context, privacy and accuracy are conflicting goals we show that a Pareto set can be found as an optimal setting for both privacy-preserving and trust-enabling mechanisms. We show that this Pareto set, when used as the configuration for measuring the accuracy of base collaborative filtering engine, yields an optimized tradeoff between conflicting goals of privacy and accuracy. We prove this concept along with applicability of our framework by experimenting with accuracy and privacy factors, and we show through experiment how such optimal set can be inferred.

Deployment of Recommender Systems: Operational and Strategic Issues

ERIC Educational Resources Information Center

Ghoshal, Abhijeet

2011-01-01

E-commerce firms are increasingly adopting recommendation systems to effectively target customers with products and services. The first essay examines the impact that improving a recommender system has on firms that deploy such systems. A market with customers heterogeneous in their search costs is considered. We find that in a monopoly, a firm…

A Personalized Recommendation-Based Mobile Learning Approach to Improving the Reading Performance of EFL Students

ERIC Educational Resources Information Center

Hsu, Ching-Kun; Hwang, Gwo-Jen; Chang, Chih-Kai

2013-01-01

In this paper, a personalized recommendation-based mobile language learning approach is proposed. A mobile learning system has been developed based on the approach by providing a reading material recommendation mechanism for guiding EFL (English as Foreign Language) students to read articles that match their preferences and knowledge levels, and a…

Crowd Sensing-Enabling Security Service Recommendation for Social Fog Computing Systems.

PubMed

Wu, Jun; Su, Zhou; Wang, Shen; Li, Jianhua

2017-07-30

Fog computing, shifting intelligence and resources from the remote cloud to edge networks, has the potential of providing low-latency for the communication from sensing data sources to users. For the objects from the Internet of Things (IoT) to the cloud, it is a new trend that the objects establish social-like relationships with each other, which efficiently brings the benefits of developed sociality to a complex environment. As fog service become more sophisticated, it will become more convenient for fog users to share their own services, resources, and data via social networks. Meanwhile, the efficient social organization can enable more flexible, secure, and collaborative networking. Aforementioned advantages make the social network a potential architecture for fog computing systems. In this paper, we design an architecture for social fog computing, in which the services of fog are provisioned based on "friend" relationships. To the best of our knowledge, this is the first attempt at an organized fog computing system-based social model. Meanwhile, social networking enhances the complexity and security risks of fog computing services, creating difficulties of security service recommendations in social fog computing. To address this, we propose a novel crowd sensing-enabling security service provisioning method to recommend security services accurately in social fog computing systems. Simulation results show the feasibilities and efficiency of the crowd sensing-enabling security service recommendation method for social fog computing systems.

Mental Health Smartphone Apps: Review and Evidence-Based Recommendations for Future Developments.

PubMed

Bakker, David; Kazantzis, Nikolaos; Rickwood, Debra; Rickard, Nikki

2016-03-01

The number of mental health apps (MHapps) developed and now available to smartphone users has increased in recent years. MHapps and other technology-based solutions have the potential to play an important part in the future of mental health care; however, there is no single guide for the development of evidence-based MHapps. Many currently available MHapps lack features that would greatly improve their functionality, or include features that are not optimized. Furthermore, MHapp developers rarely conduct or publish trial-based experimental validation of their apps. Indeed, a previous systematic review revealed a complete lack of trial-based evidence for many of the hundreds of MHapps available. To guide future MHapp development, a set of clear, practical, evidence-based recommendations is presented for MHapp developers to create better, more rigorous apps. A literature review was conducted, scrutinizing research across diverse fields, including mental health interventions, preventative health, mobile health, and mobile app design. Sixteen recommendations were formulated. Evidence for each recommendation is discussed, and guidance on how these recommendations might be integrated into the overall design of an MHapp is offered. Each recommendation is rated on the basis of the strength of associated evidence. It is important to design an MHapp using a behavioral plan and interactive framework that encourages the user to engage with the app; thus, it may not be possible to incorporate all 16 recommendations into a single MHapp. Randomized controlled trials are required to validate future MHapps and the principles upon which they are designed, and to further investigate the recommendations presented in this review. Effective MHapps are required to help prevent mental health problems and to ease the burden on health systems.

Earth Observing System, Conclusions and Recommendations

NASA Technical Reports Server (NTRS)

1984-01-01

The following Earth Observing Systems (E.O.S.) recommendations were suggested: (1) a program must be initiated to ensure that present time series of Earth science data are maintained and continued. (2) A data system that provides easy, integrated, and complete access to past, present, and future data must be developed as soon as possible. (3) A long term research effort must be sustained to study and understand these time series of Earth observations. (4) The E.O.S. should be established as an information system to carry out those aspects of the above recommendations which go beyond existing and currently planned activities. (5) The scientific direction of the E.O.S. should be established and continued through an international scientific steering committee.

Personalized recommendation based on heat bidirectional transfer

NASA Astrophysics Data System (ADS)

Ma, Wenping; Feng, Xiang; Wang, Shanfeng; Gong, Maoguo

2016-02-01

Personalized recommendation has become an increasing popular research topic, which aims to find future likes and interests based on users' past preferences. Traditional recommendation algorithms pay more attention to forecast accuracy by calculating first-order relevance, while ignore the importance of diversity and novelty that provide comfortable experiences for customers. There are some levels of contradictions between these three metrics, so an algorithm based on bidirectional transfer is proposed in this paper to solve this dilemma. In this paper, we agree that an object that is associated with history records or has been purchased by similar users should be introduced to the specified user and recommendation approach based on heat bidirectional transfer is proposed. Compared with the state-of-the-art approaches based on bipartite network, experiments on two benchmark data sets, Movielens and Netflix, demonstrate that our algorithm has better performance on accuracy, diversity and novelty. Moreover, this method does better in exploiting long-tail commodities and cold-start problem.

The recommender system for virtual items in MMORPGs based on a novel collaborative filtering approach

NASA Astrophysics Data System (ADS)

Li, S. G.; Shi, L.

2014-10-01

The recommendation system for virtual items in massive multiplayer online role-playing games (MMORPGs) has aroused the interest of researchers. Of the many approaches to construct a recommender system, collaborative filtering (CF) has been the most successful one. However, the traditional CFs just lure customers into the purchasing action and overlook customers' satisfaction, moreover, these techniques always suffer from low accuracy under cold-start conditions. Therefore, a novel collaborative filtering (NCF) method is proposed to identify like-minded customers according to the preference similarity coefficient (PSC), which implies correlation between the similarity of customers' characteristics and the similarity of customers' satisfaction level for the product. Furthermore, the analytic hierarchy process (AHP) is used to determine the relative importance of each characteristic of the customer and the improved ant colony optimisation (IACO) is adopted to generate the expression of the PSC. The IACO creates solutions using the Markov random walk model, which can accelerate the convergence of algorithm and prevent prematurity. For a target customer whose neighbours can be found, the NCF can predict his satisfaction level towards the suggested products and recommend the acceptable ones. Under cold-start conditions, the NCF will generate the recommendation list by excluding items that other customers prefer.

Dynamic Grover search: applications in recommendation systems and optimization problems

NASA Astrophysics Data System (ADS)

Chakrabarty, Indranil; Khan, Shahzor; Singh, Vanshdeep

2017-06-01

In the recent years, we have seen that Grover search algorithm (Proceedings, 28th annual ACM symposium on the theory of computing, pp. 212-219, 1996) by using quantum parallelism has revolutionized the field of solving huge class of NP problems in comparisons to classical systems. In this work, we explore the idea of extending Grover search algorithm to approximate algorithms. Here we try to analyze the applicability of Grover search to process an unstructured database with a dynamic selection function in contrast to the static selection function used in the original work (Grover in Proceedings, 28th annual ACM symposium on the theory of computing, pp. 212-219, 1996). We show that this alteration facilitates us to extend the application of Grover search to the field of randomized search algorithms. Further, we use the dynamic Grover search algorithm to define the goals for a recommendation system based on which we propose a recommendation algorithm which uses binomial similarity distribution space giving us a quadratic speedup over traditional classical unstructured recommendation systems. Finally, we see how dynamic Grover search can be used to tackle a wide range of optimization problems where we improve complexity over existing optimization algorithms.

Using a Recommendation System to Support Problem Solving and Case-Based Reasoning Retrieval

ERIC Educational Resources Information Center

Tawfik, Andrew A.; Alhoori, Hamed; Keene, Charles Wayne; Bailey, Christian; Hogan, Maureen

2018-01-01

In case library learning environments, learners are presented with an array of narratives that can be used to guide their problem solving. However, according to theorists, learners struggle to identify and retrieve the optimal case to solve a new problem. Given the challenges novice face during case retrieval, recommender systems can be embedded…

Effects of Different Surface Treatment Methods and MDP Monomer on Resin Cementation of Zirconia Ceramics an In Vitro Study

PubMed Central

Tanış, Merve Çakırbay; Akçaboy, Cihan

2015-01-01

Introduction: Resin cements are generally preferred for cementation of zirconia ceramics. Resin bonding of zirconia ceramics cannot be done with the same methods of traditional ceramics because zirconia is a silica-free material. In recent years, many methods have been reported in the literature to provide the resin bonding of zirconia ceramics. The purpose of this in vitro study is to evaluate effects of different surface treatments and 10-metacryloxydecyl dihydrogen phosphate (MDP) monomer on shear bond strength between zirconia and resin cement. Methods: 120 zirconia specimens were treated as follows: Group I: sandblasting, group II: sandblasting + tribochemical silica coating + silane, group III: sandblasting + Nd:YAG (neodymium: yttrium-aluminum-garnet) laser. One specimen from each group was evaluated under scanning electron microscope (SEM). Specimens in each group were bonded either with conventional resin cement Variolink II or with a MDP containing resin cement Panavia F2.0. Subgroups of bonded specimens were stored in distilled water (37°C) for 24 hours or 14 days. Following water storage shear bond strength test was performed at a crosshead speed of 1 mm/min in a universal test machine. Then statistical analyses were performed. Results: Highest shear bond strength values were observed in group II. No significant difference between group I and III was found when Panavia F2.0 resin cement was used. When Variolink II resin cement was used group III showed significantly higher bond strength than group I. In group I, Panavia F2.0 resin cement showed statistically higher shear bond strength than Variolink II resin cement. In group II no significant difference was found between resin cements. No significant difference was found between specimens stored in 37°C distilled water for 24 hours and 14 days. In group I surface irregularities with sharp edges and grooves were observed. In group II less roughened surface was observed with silica particles. In group

Effects of Different Surface Treatment Methods and MDP Monomer on Resin Cementation of Zirconia Ceramics an In Vitro Study.

PubMed

Tanış, Merve Çakırbay; Akçaboy, Cihan

2015-01-01

Resin cements are generally preferred for cementation of zirconia ceramics. Resin bonding of zirconia ceramics cannot be done with the same methods of traditional ceramics because zirconia is a silica-free material. In recent years, many methods have been reported in the literature to provide the resin bonding of zirconia ceramics. The purpose of this in vitro study is to evaluate effects of different surface treatments and 10-metacryloxydecyl dihydrogen phosphate (MDP) monomer on shear bond strength between zirconia and resin cement. 120 zirconia specimens were treated as follows: Group I: sandblasting, group II: sandblasting + tribochemical silica coating + silane, group III: sandblasting + Nd:YAG (neodymium: yttrium-aluminum-garnet) laser. One specimen from each group was evaluated under scanning electron microscope (SEM). Specimens in each group were bonded either with conventional resin cement Variolink II or with a MDP containing resin cement Panavia F2.0. Subgroups of bonded specimens were stored in distilled water (37°C) for 24 hours or 14 days. Following water storage shear bond strength test was performed at a crosshead speed of 1 mm/min in a universal test machine. Then statistical analyses were performed. Highest shear bond strength values were observed in group II. No significant difference between group I and III was found when Panavia F2.0 resin cement was used. When Variolink II resin cement was used group III showed significantly higher bond strength than group I. In group I, Panavia F2.0 resin cement showed statistically higher shear bond strength than Variolink II resin cement. In group II no significant difference was found between resin cements. No significant difference was found between specimens stored in 37°C distilled water for 24 hours and 14 days. In group I surface irregularities with sharp edges and grooves were observed. In group II less roughened surface was observed with silica particles. In group III surface microcracks

Evaluating Recommender Systems for Technology Enhanced Learning: A Quantitative Survey

ERIC Educational Resources Information Center

Erdt, Mojisola; Fernandez, Alejandro; Rensing, Christoph

2015-01-01

The increasing number of publications on recommender systems for Technology Enhanced Learning (TEL) evidence a growing interest in their development and deployment. In order to support learning, recommender systems for TEL need to consider specific requirements, which differ from the requirements for recommender systems in other domains like…

Which patients with myelofibrosis should receive ruxolitinib therapy? ELN-SIE evidence-based recommendations.

PubMed

Marchetti, M; Barosi, G; Cervantes, F; Birgegård, G; Griesshammer, M; Harrison, C; Hehlmann, R; Kiladjian, J-J; Kröger, N; McMullin, M F; Passamonti, F; Vannucchi, A; Barbui, T

2017-04-01

Ruxolitinib is an oral Janus-activated kinase 1 (JAK1)/JAK2 inhibitor approved for the treatment of patients with myelofibrosis based on the results of two randomized clinical trials. However, discordant indications were provided by regulatory agencies and scientific societies for selecting the most appropriate candidates to this drug. The European LeukemiaNet and the Italian Society of Hematology shared the aim of building evidence-based recommendations for the use of ruxolitinib according to the GRADE methodology. Eighteen patient-intervention-comparator-outcome profiles were listed, each of them comparing ruxolitinib to other therapies with the aim of improving one of the three clinical outcomes: (a) splenomegaly, (b) disease-related symptoms, and (c) survival. Ruxolitinib was strongly recommended for improving symptomatic or severe (>15 cm below the costal margin) splenomegaly in patients with an International Prognostic Scoring System (IPSS)/dynamic IPSS risk intermediate 2 or high. Ruxolitinib was also strongly recommended for improving systemic symptoms in patients with an MPN10 score >44, refractory severe itching, unintended weight loss not attributable to other causes or unexplained fever. Because of weak evidence, the panel does not recommend ruxolitinib therapy for improving survival. Also, the recommendations given above do not necessarily apply to patients who are candidates for allogeneic stem cell transplant.

Mental Health Smartphone Apps: Review and Evidence-Based Recommendations for Future Developments

PubMed Central

Kazantzis, Nikolaos; Rickwood, Debra; Rickard, Nikki

2016-01-01

Background The number of mental health apps (MHapps) developed and now available to smartphone users has increased in recent years. MHapps and other technology-based solutions have the potential to play an important part in the future of mental health care; however, there is no single guide for the development of evidence-based MHapps. Many currently available MHapps lack features that would greatly improve their functionality, or include features that are not optimized. Furthermore, MHapp developers rarely conduct or publish trial-based experimental validation of their apps. Indeed, a previous systematic review revealed a complete lack of trial-based evidence for many of the hundreds of MHapps available. Objective To guide future MHapp development, a set of clear, practical, evidence-based recommendations is presented for MHapp developers to create better, more rigorous apps. Methods A literature review was conducted, scrutinizing research across diverse fields, including mental health interventions, preventative health, mobile health, and mobile app design. Results Sixteen recommendations were formulated. Evidence for each recommendation is discussed, and guidance on how these recommendations might be integrated into the overall design of an MHapp is offered. Each recommendation is rated on the basis of the strength of associated evidence. It is important to design an MHapp using a behavioral plan and interactive framework that encourages the user to engage with the app; thus, it may not be possible to incorporate all 16 recommendations into a single MHapp. Conclusions Randomized controlled trials are required to validate future MHapps and the principles upon which they are designed, and to further investigate the recommendations presented in this review. Effective MHapps are required to help prevent mental health problems and to ease the burden on health systems. PMID:26932350

A Recommendation System to Facilitate Business Process Modeling.

PubMed

Deng, Shuiguang; Wang, Dongjing; Li, Ying; Cao, Bin; Yin, Jianwei; Wu, Zhaohui; Zhou, Mengchu

2017-06-01

This paper presents a system that utilizes process recommendation technology to help design new business processes from scratch in an efficient and accurate way. The proposed system consists of two phases: 1) offline mining and 2) online recommendation. At the first phase, it mines relations among activity nodes from existing processes in repository, and then stores the extracted relations as patterns in a database. At the second phase, it compares the new process under construction with the premined patterns, and recommends proper activity nodes of the most matching patterns to help build a new process. Specifically, there are three different online recommendation strategies in this system. Experiments on both real and synthetic datasets are conducted to compare the proposed approaches with the other state-of-the-art ones, and the results show that the proposed approaches outperform them in terms of accuracy and efficiency.

Crowd Sensing-Enabling Security Service Recommendation for Social Fog Computing Systems

PubMed Central

Wu, Jun; Su, Zhou; Li, Jianhua

2017-01-01

Fog computing, shifting intelligence and resources from the remote cloud to edge networks, has the potential of providing low-latency for the communication from sensing data sources to users. For the objects from the Internet of Things (IoT) to the cloud, it is a new trend that the objects establish social-like relationships with each other, which efficiently brings the benefits of developed sociality to a complex environment. As fog service become more sophisticated, it will become more convenient for fog users to share their own services, resources, and data via social networks. Meanwhile, the efficient social organization can enable more flexible, secure, and collaborative networking. Aforementioned advantages make the social network a potential architecture for fog computing systems. In this paper, we design an architecture for social fog computing, in which the services of fog are provisioned based on “friend” relationships. To the best of our knowledge, this is the first attempt at an organized fog computing system-based social model. Meanwhile, social networking enhances the complexity and security risks of fog computing services, creating difficulties of security service recommendations in social fog computing. To address this, we propose a novel crowd sensing-enabling security service provisioning method to recommend security services accurately in social fog computing systems. Simulation results show the feasibilities and efficiency of the crowd sensing-enabling security service recommendation method for social fog computing systems. PMID:28758943

Security Considerations and Recommendations in Computer-Based Testing

PubMed Central

Al-Saleem, Saleh M.

2014-01-01

Many organizations and institutions around the globe are moving or planning to move their paper-and-pencil based testing to computer-based testing (CBT). However, this conversion will not be the best option for all kinds of exams and it will require significant resources. These resources may include the preparation of item banks, methods for test delivery, procedures for test administration, and last but not least test security. Security aspects may include but are not limited to the identification and authentication of examinee, the risks that are associated with cheating on the exam, and the procedures related to test delivery to the examinee. This paper will mainly investigate the security considerations associated with CBT and will provide some recommendations for the security of these kinds of tests. We will also propose a palm-based biometric authentication system incorporated with basic authentication system (username/password) in order to check the identity and authenticity of the examinee. PMID:25254250

Security considerations and recommendations in computer-based testing.

PubMed

Al-Saleem, Saleh M; Ullah, Hanif

2014-01-01

Many organizations and institutions around the globe are moving or planning to move their paper-and-pencil based testing to computer-based testing (CBT). However, this conversion will not be the best option for all kinds of exams and it will require significant resources. These resources may include the preparation of item banks, methods for test delivery, procedures for test administration, and last but not least test security. Security aspects may include but are not limited to the identification and authentication of examinee, the risks that are associated with cheating on the exam, and the procedures related to test delivery to the examinee. This paper will mainly investigate the security considerations associated with CBT and will provide some recommendations for the security of these kinds of tests. We will also propose a palm-based biometric authentication system incorporated with basic authentication system (username/password) in order to check the identity and authenticity of the examinee.

A collaborative approach for research paper recommender system.

PubMed

Haruna, Khalid; Akmar Ismail, Maizatul; Damiasih, Damiasih; Sutopo, Joko; Herawan, Tutut

2017-01-01

Research paper recommenders emerged over the last decade to ease finding publications relating to researchers' area of interest. The challenge was not just to provide researchers with very rich publications at any time, any place and in any form but to also offer the right publication to the right researcher in the right way. Several approaches exist in handling paper recommender systems. However, these approaches assumed the availability of the whole contents of the recommending papers to be freely accessible, which is not always true due to factors such as copyright restrictions. This paper presents a collaborative approach for research paper recommender system. By leveraging the advantages of collaborative filtering approach, we utilize the publicly available contextual metadata to infer the hidden associations that exist between research papers in order to personalize recommendations. The novelty of our proposed approach is that it provides personalized recommendations regardless of the research field and regardless of the user's expertise. Using a publicly available dataset, our proposed approach has recorded a significant improvement over other baseline methods in measuring both the overall performance and the ability to return relevant and useful publications at the top of the recommendation list.

A collaborative approach for research paper recommender system

PubMed Central

Akmar Ismail, Maizatul; Damiasih, Damiasih; Sutopo, Joko; Herawan, Tutut

2017-01-01

Research paper recommenders emerged over the last decade to ease finding publications relating to researchers’ area of interest. The challenge was not just to provide researchers with very rich publications at any time, any place and in any form but to also offer the right publication to the right researcher in the right way. Several approaches exist in handling paper recommender systems. However, these approaches assumed the availability of the whole contents of the recommending papers to be freely accessible, which is not always true due to factors such as copyright restrictions. This paper presents a collaborative approach for research paper recommender system. By leveraging the advantages of collaborative filtering approach, we utilize the publicly available contextual metadata to infer the hidden associations that exist between research papers in order to personalize recommendations. The novelty of our proposed approach is that it provides personalized recommendations regardless of the research field and regardless of the user’s expertise. Using a publicly available dataset, our proposed approach has recorded a significant improvement over other baseline methods in measuring both the overall performance and the ability to return relevant and useful publications at the top of the recommendation list. PMID:28981512

Do Nutrient-Based Front-of-Pack Labelling Schemes Support or Undermine Food-Based Dietary Guideline Recommendations? Lessons from the Australian Health Star Rating System.

PubMed

Lawrence, Mark A; Dickie, Sarah; Woods, Julie L

2018-01-05

Food-based Dietary Guidelines (FBDGs) promote healthy dietary patterns. Nutrient-based Front-of-Pack Labelling (NBFOPL) schemes rate the 'healthiness' of individual foods. This study aimed to investigate whether the Australian Health Star Rating (HSR) system aligns with the Australian Dietary Guidelines (ADGs). The Mintel Global New Products Database was searched for every new food product displaying a HSR entering the Australian marketplace from 27 June 2014 (HSR system endorsement) until 30 June 2017. Foods were categorised as either a five food group (FFG) food or 'discretionary' food in accordance with ADG recommendations. Ten percent (1269/12,108) of new food products displayed a HSR, of which 57% were FFG foods. The median number of 'health' stars displayed on discretionary foods (2.5; range: 0.5-5) was significantly lower ( p < 0.05) than FFG foods (4.0; range: 0.5-5), although a high frequency of anomalies and overlap in the number of stars across the two food categories was observed, with 56.7% of discretionary foods displaying ≥2.5 stars. The HSR system is undermining the ADG recommendations through facilitating the marketing of discretionary foods. Adjusting the HSR's algorithm might correct certain technical flaws. However, supporting the ADGs requires reform of the HSR's design to demarcate the food source (FFG versus discretionary food) of a nutrient.

Tailoring implementation strategies for evidence-based recommendations using computerised clinical decision support systems: protocol for the development of the GUIDES tools.

PubMed

Van de Velde, Stijn; Roshanov, Pavel; Kortteisto, Tiina; Kunnamo, Ilkka; Aertgeerts, Bert; Vandvik, Per Olav; Flottorp, Signe

2016-03-05

A computerised clinical decision support system (CCDSS) is a technology that uses patient-specific data to provide relevant medical knowledge at the point of care. It is considered to be an important quality improvement intervention, and the implementation of CCDSS is growing substantially. However, the significant investments do not consistently result in value for money due to content, context, system and implementation issues. The Guideline Implementation with Decision Support (GUIDES) project aims to improve the impact of CCDSS through optimised implementation based on high-quality evidence-based recommendations. To achieve this, we will develop tools that address the factors that determine successful CCDSS implementation. We will develop the GUIDES tools in four steps, using the methods and results of the Tailored Implementation for Chronic Diseases (TICD) project as a starting point: (1) a review of research evidence and frameworks on the determinants of implementing recommendations using CCDSS; (2) a synthesis of a comprehensive framework for the identified determinants; (3) the development of tools for use of the framework and (4) pilot testing the utility of the tools through the development of a tailored CCDSS intervention in Norway, Belgium and Finland. We selected the conservative management of knee osteoarthritis as a prototype condition for the pilot. During the process, the authors will collaborate with an international expert group to provide input and feedback on the tools. This project will provide guidance and tools on methods of identifying implementation determinants and selecting strategies to implement evidence-based recommendations through CCDSS. We will make the GUIDES tools available to CCDSS developers, implementers, researchers, funders, clinicians, managers, educators, and policymakers internationally. The tools and recommendations will be generic, which makes them scalable to a large spectrum of conditions. Ultimately, the better

Implementation of Recommendations from the One System Comparative Evaluation of the Hanford Tank Farms and Waste Treatment Plant Safety Bases

DOE Office of Scientific and Technical Information (OSTI.GOV)

Garrett, Richard L.; Niemi, Belinda J.; Paik, Ingle K.

2013-11-07

A Comparative Evaluation was conducted for One System Integrated Project Team to compare the safety bases for the Hanford Waste Treatment and Immobilization Plant Project (WTP) and Tank Operations Contract (TOC) (i.e., Tank Farms) by an Expert Review Team. The evaluation had an overarching purpose to facilitate effective integration between WTP and TOC safety bases. It was to provide One System management with an objective evaluation of identified differences in safety basis process requirements, guidance, direction, procedures, and products (including safety controls, key safety basis inputs and assumptions, and consequence calculation methodologies) between WTP and TOC. The evaluation identified 25more » recommendations (Opportunities for Integration). The resolution of these recommendations resulted in 16 implementation plans. The completion of these implementation plans will help ensure consistent safety bases for WTP and TOC along with consistent safety basis processes. procedures, and analyses. and should increase the likelihood of a successful startup of the WTP. This early integration will result in long-term cost savings and significant operational improvements. In addition, the implementation plans lead to the development of eight new safety analysis methodologies that can be used at other U.S. Department of Energy (US DOE) complex sites where URS Corporation is involved.« less

Integrating Information Extraction Agents into a Tourism Recommender System

NASA Astrophysics Data System (ADS)

Esparcia, Sergio; Sánchez-Anguix, Víctor; Argente, Estefanía; García-Fornes, Ana; Julián, Vicente

Recommender systems face some problems. On the one hand information needs to be maintained updated, which can result in a costly task if it is not performed automatically. On the other hand, it may be interesting to include third party services in the recommendation since they improve its quality. In this paper, we present an add-on for the Social-Net Tourism Recommender System that uses information extraction and natural language processing techniques in order to automatically extract and classify information from the Web. Its goal is to maintain the system updated and obtain information about third party services that are not offered by service providers inside the system.

Which Doctor to Trust: A Recommender System for Identifying the Right Doctors.

PubMed

Guo, Li; Jin, Bo; Yao, Cuili; Yang, Haoyu; Huang, Degen; Wang, Fei

2016-07-07

Key opinion leaders (KOLs) are people who can influence public opinion on a certain subject matter. In the field of medical and health informatics, it is critical to identify KOLs on various disease conditions. However, there have been very few studies on this topic. We aimed to develop a recommender system for identifying KOLs for any specific disease with health care data mining. We exploited an unsupervised aggregation approach for integrating various ranking features to identify doctors who have the potential to be KOLs on a range of diseases. We introduce the design, implementation, and deployment details of the recommender system. This system collects the professional footprints of doctors, such as papers in scientific journals, presentation activities, patient advocacy, and media exposure, and uses them as ranking features to identify KOLs. We collected the information of 2,381,750 doctors in China from 3,657,797 medical journal papers they published, together with their profiles, academic publications, and funding. The empirical results demonstrated that our system outperformed several benchmark systems by a significant margin. Moreover, we conducted a case study in a real-world system to verify the applicability of our proposed method. Our results show that doctors' profiles and their academic publications are key data sources for identifying KOLs in the field of medical and health informatics. Moreover, we deployed the recommender system and applied the data service to a recommender system of the China-based Internet technology company NetEase. Patients can obtain authority ranking lists of doctors with this system on any given disease.

Bond strength of primer/cement systems to zirconia subjected to artificial aging.

PubMed

Zhao, Li; Jian, Yu-Tao; Wang, Xiao-Dong; Zhao, Ke

2016-11-01

Creating reliable and durable adhesion to the nonactive zirconia surface is difficult and has limited zirconia use. The introduction of functional monomers such as 10-methacryloyloxydecyl dihydrogen phosphate (MDP) appears to have enhanced bond strength to zirconia. The purpose of this in vitro study was to evaluate the long-term bond strength of several MDP-containing primer/cement systems to zirconia. Zirconia blocks were divided into 6 groups (n=24) according to the 3 primers/cements to be bonded, as follows: Scotchbond Universal/RelyX Ultimate (SU/RU; consisting of MDP-containing primer/MDP-free cement); Clearfil ceramic primer/Panavia F (CCP/PAN; consisting ofMDP-containing/MDP-containing); and Z-Prime Plus/Duo-Link (ZP/DUO; consisting ofMDP-containing/MDP-free), which were compared with 3 nonprimed groups, RU, PAN, and DUO. After bonding, each group was further divided into 3 subgroups (n=8) according to the level of aging: 24-hour storage in water at 37°C (24H); 30-day storage at 37°C (30D); and 30-day storage at 37°C followed by 3000 thermal cycles (30D/TC). After aging, a shear bond strength test and failure mode analysis were performed. The data were analyzed using 2-way ANOVA (α=.05). After aging, nearly all primer/cement groups presented significantly higher bond strength than the related nonprimed groups for each level of aging (P<.05), except for CCP/PAN versus PAN with 24H (P=.741). SU/RU had the highest bond strength among the groups for all treatments (P<.05), except for CCP/PAN versus SU/RU with 30D/TC (P=.171). Among the nonprimed groups, only RU went through 30D/TC without premature debonding. With 24H and 30D, the failure modes in SU/RU and CCP/PAN were purely mixed, whereas those in the other groups were mainly adhesive, except for RU. The superiority of the initial bond strength in SU/RU may result from some functional components other than MDP. The presence of MDP in the cement did not appear to have a positive effect on long-term bond

The Impact of Youth and Family Risk Factors on Service Recommendations and Delivery in a School-Based System of Care

PubMed Central

Whitson, Melissa L.; Connell, Christian M.; Bernard, Stanley; Kaufman, Joy S.

2010-01-01

The present study examines the impact of child and family risk factors on service access for youth and families in a school-based system of care. Regression analyses examined the relationships between risk factors and services recommended, services received, and dosage of services received. Logistic regression analyses examined the relationship between risk factors and whether or not youth received specific types of services within the system of care. Results revealed that youth with a personal or family history of substance use had more services recommended than youth without these risk factors, while youth with a family history of substance use received more services. Youth with a history of substance use received a significantly higher dosage of services overall. Finally, history of family mental illness was associated with receiving mental health and operational services (e.g., family advocacy, emergency funds). Implications and limitations are discussed. PMID:20165927

Very-Low-Cost, Rugged Vacuum System

NASA Technical Reports Server (NTRS)

Kline-Schoder, Robert; Sorensen, Paul; Passow, Christian; Bilski, Steve

2013-01-01

NASA, DoD, DHS, and commercial industry have a need for miniaturized, rugged, low-cost vacuum systems. Recent advances in sensor technology have led to the development of very small mass spectrometer detectors as well as other miniature analytical instruments. However, the vacuum systems to support these sensors remain large, heavy, and power-hungry. To meet this need, a miniaturized vacuum system was created based on a very small, rugged, and inexpensive- to-manufacture molecular drag pump (MDP). The MDP is enabled by the development of a miniature, veryhigh- speed, rugged, low-power, brushless DC motor optimized for wide temperature operation and long life. Such a pump represents an order-of-magnitude reduction in mass, volume, and cost over current, commercially available, state-ofthe- art vacuum pumps. The vacuum system consists of the MDP coupled to a ruggedized rough pump (for terrestrial applications or for planets with substantial atmospheres). The rotor in the MDP consists of a simple smooth cylinder of aluminum spinning at approximately 200,000 RPM inside an outer stator housing. The pump stator comprises a cylindrical aluminum housing with one or more specially designed grooves that serve as flow channels. To minimize the length of the pump, the gas is forced down the flow channels of the outer stator to the base of the pump. The gas is then turned and pulled toward the top through a second set of channels cut into an inner stator housing that surrounds the motor. The compressed gas then flows down channels in the motor housing to the exhaust port of the pump. The exhaust port of the pump is connected to a diaphragm or scroll pump. This pump delivers very high performance in a very small envelope. The design was simplified so that a smaller compression ratio, easier manufacturing process, and enhanced ruggedness can be achieved at the lowest possible cost. The machining of the rotor and stators is very simple compared to that necessary to fabricate TMP

A novel microdialysis-dissolution/permeation system for testing oral dosage forms: A proof-of-concept study.

PubMed

Fong, Sophia Yui Kau; Poulsen, Jessie; Brandl, Martin; Bauer-Brandl, Annette

2017-01-01

A novel microdialysis-dissolution/permeation (M-D/P) system was developed for the biopharmaceutical assessment of oral drug formulations. This system consists of a side-by-side diffusion chamber, a microdialysis unit fixed within the dissolution chamber for continuous sampling, and a biomimetic Permeapad® as the intestinal barrier. In the M-D/P system, the concentration of the molecularly dissolved drug (with MWCO <20kDa) was measured over time in the dissolution compartment (representing the gastrointestinal tract) while the concentration of the permeated drug was measured in the acceptor compartment (representing the blood). The kinetics of both the dissolution process and the permeation process were simultaneously quantified under circumstances that mimic physiological conditions. For the current proof-of-concept study, hydrocortisone (HCS) in the form of slowly dissolving solvate crystals and buffer and the biorelevant fasted state simulated intestinal fluids (FaSSIF), were employed as the model drug and dissolution media, respectively. The applicability of the M-D/P system to dissolution and permeation profiling of HCS in buffer and in FaSSIF has been successfully demonstrated. Compared to the conventional direct sampling method (using filter of 0.1-0.45μm), sampling by the M-D/P system exhibited distinct advantages, including (1) showing minimal disturbance of the permeation process, (2) differentiating "molecularly" dissolved drugs from "apparently" dissolved drugs during dissolution of HCS in FaSSIF, and (3) being less laborious and having better sampling temporal resolution. M-D/P system appeared to be a promising, simple and routine tool that allows for the researchers' intensive comprehension of the interplay of dissolution and permeation thus helping for better oral formulation screening and as an ultimate goal, for better dosage forms assessment. Copyright © 2016. Published by Elsevier B.V.

Personalized recommendation based on preferential bidirectional mass diffusion

NASA Astrophysics Data System (ADS)

Chen, Guilin; Gao, Tianrun; Zhu, Xuzhen; Tian, Hui; Yang, Zhao

2017-03-01

Recommendation system provides a promising way to alleviate the dilemma of information overload. In physical dynamics, mass diffusion has been used to design effective recommendation algorithms on bipartite network. However, most of the previous studies focus overwhelmingly on unidirectional mass diffusion from collected objects to uncollected objects, while overlooking the opposite direction, leading to the risk of similarity estimation deviation and performance degradation. In addition, they are biased towards recommending popular objects which will not necessarily promote the accuracy but make the recommendation lack diversity and novelty that indeed contribute to the vitality of the system. To overcome the aforementioned disadvantages, we propose a preferential bidirectional mass diffusion (PBMD) algorithm by penalizing the weight of popular objects in bidirectional diffusion. Experiments are evaluated on three benchmark datasets (Movielens, Netflix and Amazon) by 10-fold cross validation, and results indicate that PBMD remarkably outperforms the mainstream methods in accuracy, diversity and novelty.

Effective Trust-Aware E-learning Recommender System Based on Learning Styles and Knowledge Levels

ERIC Educational Resources Information Center

Dwivedi, Pragya; Bharadwaj, Kamal K.

2013-01-01

In the age of information explosion, e-learning recommender systems (ELRSs) have emerged as the most essential tool to deliver personalized learning resources to learners. Due to enormous amount of information on the web, learner faces problem in searching right information. ELRSs deal with the problem of information overload effectively and…

Using RNA-seq data to select reference genes for normalizing gene expression in apple roots.

PubMed

Zhou, Zhe; Cong, Peihua; Tian, Yi; Zhu, Yanmin

2017-01-01

Gene expression in apple roots in response to various stress conditions is a less-explored research subject. Reliable reference genes for normalizing quantitative gene expression data have not been carefully investigated. In this study, the suitability of a set of 15 apple genes were evaluated for their potential use as reliable reference genes. These genes were selected based on their low variance of gene expression in apple root tissues from a recent RNA-seq data set, and a few previously reported apple reference genes for other tissue types. Four methods, Delta Ct, geNorm, NormFinder and BestKeeper, were used to evaluate their stability in apple root tissues of various genotypes and under different experimental conditions. A small panel of stably expressed genes, MDP0000095375, MDP0000147424, MDP0000233640, MDP0000326399 and MDP0000173025 were recommended for normalizing quantitative gene expression data in apple roots under various abiotic or biotic stresses. When the most stable and least stable reference genes were used for data normalization, significant differences were observed on the expression patterns of two target genes, MdLecRLK5 (MDP0000228426, a gene encoding a lectin receptor like kinase) and MdMAPK3 (MDP0000187103, a gene encoding a mitogen-activated protein kinase). Our data also indicated that for those carefully validated reference genes, a single reference gene is sufficient for reliable normalization of the quantitative gene expression. Depending on the experimental conditions, the most suitable reference genes can be specific to the sample of interest for more reliable RT-qPCR data normalization.

Using RNA-seq data to select reference genes for normalizing gene expression in apple roots

PubMed Central

Zhou, Zhe; Cong, Peihua; Tian, Yi

2017-01-01

Gene expression in apple roots in response to various stress conditions is a less-explored research subject. Reliable reference genes for normalizing quantitative gene expression data have not been carefully investigated. In this study, the suitability of a set of 15 apple genes were evaluated for their potential use as reliable reference genes. These genes were selected based on their low variance of gene expression in apple root tissues from a recent RNA-seq data set, and a few previously reported apple reference genes for other tissue types. Four methods, Delta Ct, geNorm, NormFinder and BestKeeper, were used to evaluate their stability in apple root tissues of various genotypes and under different experimental conditions. A small panel of stably expressed genes, MDP0000095375, MDP0000147424, MDP0000233640, MDP0000326399 and MDP0000173025 were recommended for normalizing quantitative gene expression data in apple roots under various abiotic or biotic stresses. When the most stable and least stable reference genes were used for data normalization, significant differences were observed on the expression patterns of two target genes, MdLecRLK5 (MDP0000228426, a gene encoding a lectin receptor like kinase) and MdMAPK3 (MDP0000187103, a gene encoding a mitogen-activated protein kinase). Our data also indicated that for those carefully validated reference genes, a single reference gene is sufficient for reliable normalization of the quantitative gene expression. Depending on the experimental conditions, the most suitable reference genes can be specific to the sample of interest for more reliable RT-qPCR data normalization. PMID:28934340

Which Doctor to Trust: A Recommender System for Identifying the Right Doctors

PubMed Central

Yao, Cuili; Yang, Haoyu; Huang, Degen; Wang, Fei

2016-01-01

Background Key opinion leaders (KOLs) are people who can influence public opinion on a certain subject matter. In the field of medical and health informatics, it is critical to identify KOLs on various disease conditions. However, there have been very few studies on this topic. Objective We aimed to develop a recommender system for identifying KOLs for any specific disease with health care data mining. Methods We exploited an unsupervised aggregation approach for integrating various ranking features to identify doctors who have the potential to be KOLs on a range of diseases. We introduce the design, implementation, and deployment details of the recommender system. This system collects the professional footprints of doctors, such as papers in scientific journals, presentation activities, patient advocacy, and media exposure, and uses them as ranking features to identify KOLs. Results We collected the information of 2,381,750 doctors in China from 3,657,797 medical journal papers they published, together with their profiles, academic publications, and funding. The empirical results demonstrated that our system outperformed several benchmark systems by a significant margin. Moreover, we conducted a case study in a real-world system to verify the applicability of our proposed method. Conclusions Our results show that doctors’ profiles and their academic publications are key data sources for identifying KOLs in the field of medical and health informatics. Moreover, we deployed the recommender system and applied the data service to a recommender system of the China-based Internet technology company NetEase. Patients can obtain authority ranking lists of doctors with this system on any given disease. PMID:27390219

Feasibility of Integrated Menu Recommendation and Self-Order System for Small-Scale Restaurants

NASA Astrophysics Data System (ADS)

Kashima, Tomoko; Matsumoto, Shimpei; Ishii, Hiroaki

2010-10-01

In recent years, point of sales (POS) systems with order function have been developed for restaurants. Since expensive apparatus and system are required for installing POS systems, usually only large-scale restaurant chains can afford to introduce them. In this research, we consider the POS management in a restaurant, which cooperates with an automatic order function by using a personal digital device aiming at the safety of the food, pursuit of service, and further operational efficiency improvements, such as foods management, accounting treatment, and ordering work. In traditional POS systems, information recommendation technology is not taken into consideration. We realize the recommendation of a menu according to the user's preference using rough sets and menu planning based on stock status by applying information recommendation technology. Therefore, we believe that this system can be used in comfort with regard to freshness of foods, allergy, diabetes, etc. Furthermore, due to the reduction of the personnel expenses by an operational efficiency improvement such technology becomes even feasible for small-scale stores.

Study of data I/O performance on distributed disk system in mask data preparation

NASA Astrophysics Data System (ADS)

Ohara, Shuichiro; Odaira, Hiroyuki; Chikanaga, Tomoyuki; Hamaji, Masakazu; Yoshioka, Yasuharu

2010-09-01

Data volume is getting larger every day in Mask Data Preparation (MDP). In the meantime, faster data handling is always required. MDP flow typically introduces Distributed Processing (DP) system to realize the demand because using hundreds of CPU is a reasonable solution. However, even if the number of CPU were increased, the throughput might be saturated because hard disk I/O and network speeds could be bottlenecks. So, MDP needs to invest a lot of money to not only hundreds of CPU but also storage and a network device which make the throughput faster. NCS would like to introduce new distributed processing system which is called "NDE". NDE could be a distributed disk system which makes the throughput faster without investing a lot of money because it is designed to use multiple conventional hard drives appropriately over network. NCS studies I/O performance with OASIS® data format on NDE which contributes to realize the high throughput in this paper.

VisIRR: A Visual Analytics System for Information Retrieval and Recommendation for Large-Scale Document Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Choo, Jaegul; Kim, Hannah; Clarkson, Edward

In this paper, we present an interactive visual information retrieval and recommendation system, called VisIRR, for large-scale document discovery. VisIRR effectively combines the paradigms of (1) a passive pull through query processes for retrieval and (2) an active push that recommends items of potential interest to users based on their preferences. Equipped with an efficient dynamic query interface against a large-scale corpus, VisIRR organizes the retrieved documents into high-level topics and visualizes them in a 2D space, representing the relationships among the topics along with their keyword summary. In addition, based on interactive personalized preference feedback with regard to documents,more » VisIRR provides document recommendations from the entire corpus, which are beyond the retrieved sets. Such recommended documents are visualized in the same space as the retrieved documents, so that users can seamlessly analyze both existing and newly recommended ones. This article presents novel computational methods, which make these integrated representations and fast interactions possible for a large-scale document corpus. We illustrate how the system works by providing detailed usage scenarios. Finally, we present preliminary user study results for evaluating the effectiveness of the system.« less

VisIRR: A Visual Analytics System for Information Retrieval and Recommendation for Large-Scale Document Data

DOE PAGES

Choo, Jaegul; Kim, Hannah; Clarkson, Edward; ...

2018-01-31

In this paper, we present an interactive visual information retrieval and recommendation system, called VisIRR, for large-scale document discovery. VisIRR effectively combines the paradigms of (1) a passive pull through query processes for retrieval and (2) an active push that recommends items of potential interest to users based on their preferences. Equipped with an efficient dynamic query interface against a large-scale corpus, VisIRR organizes the retrieved documents into high-level topics and visualizes them in a 2D space, representing the relationships among the topics along with their keyword summary. In addition, based on interactive personalized preference feedback with regard to documents,more » VisIRR provides document recommendations from the entire corpus, which are beyond the retrieved sets. Such recommended documents are visualized in the same space as the retrieved documents, so that users can seamlessly analyze both existing and newly recommended ones. This article presents novel computational methods, which make these integrated representations and fast interactions possible for a large-scale document corpus. We illustrate how the system works by providing detailed usage scenarios. Finally, we present preliminary user study results for evaluating the effectiveness of the system.« less

A trust-based recommendation method using network diffusion processes

NASA Astrophysics Data System (ADS)

Chen, Ling-Jiao; Gao, Jian

2018-09-01

A variety of rating-based recommendation methods have been extensively studied including the well-known collaborative filtering approaches and some network diffusion-based methods, however, social trust relations are not sufficiently considered when making recommendations. In this paper, we contribute to the literature by proposing a trust-based recommendation method, named CosRA+T, after integrating the information of trust relations into the resource-redistribution process. Specifically, a tunable parameter is used to scale the resources received by trusted users before the redistribution back to the objects. Interestingly, we find an optimal scaling parameter for the proposed CosRA+T method to achieve its best recommendation accuracy, and the optimal value seems to be universal under several evaluation metrics across different datasets. Moreover, results of extensive experiments on the two real-world rating datasets with trust relations, Epinions and FriendFeed, suggest that CosRA+T has a remarkable improvement in overall accuracy, diversity and novelty. Our work takes a step towards designing better recommendation algorithms by employing multiple resources of social network information.

Decision-Guided Recommenders with Composite Alternatives

ERIC Educational Resources Information Center

Alodhaibi, Khalid

2011-01-01

Recommender systems aim to support users in their decision-making process while interacting with large information spaces and recommend items of interest to users based on preferences they have expressed, either explicitly or implicitly. Recommender systems are increasingly used with product and service selection over the Internet. Although…

Recommendations for the classification of HIV associated neuromanifestations in the German DRG system.

PubMed

Evers, Stefan; Fiori, W; Brockmeyer, N; Arendt, G; Husstedt, I-W

2005-09-12

HIV associated neuromanifestations are of growing importance in the in-patient treatment of HIV infected patients. In Germany, all in-patients have to be coded according to the ICD-10 classification and the German DRG-system. We present recommendations how to code the different primary and secondary neuromanifestations of HIV infection. These recommendations are based on the commentary of the German DRG procedures and are aimed to establish uniform coding of neuromanifestations.

Consumers’ intention to use health recommendation systems to receive personalized nutrition advice

PubMed Central

2013-01-01

Background Sophisticated recommendation systems are used more and more in the health sector to assist consumers in healthy decision making. In this study we investigate consumers' evaluation of hypothetical health recommendation systems that provide personalized nutrition advice. We examine consumers' intention to use such a health recommendation system as a function of options related to the underlying system (e.g. the type of company that generates the advice) as well as intermediaries (e.g. general practitioner) that might assist in using the system. We further explore if the effect of both the system and intermediaries on intention to use a health recommendation system are mediated by consumers' perceived effort, privacy risk, usefulness and enjoyment. Methods 204 respondents from a consumer panel in the Netherlands participated. The data were collected by means of a questionnaire. Each respondent evaluated three hypothetical health recommendation systems on validated multi-scale measures of effort, privacy risk, usefulness, enjoyment and intention to use the system. To test the hypothesized relationships we used regression analyses. Results We find evidence that the options related to the underlying system as well as the intermediaries involved influence consumers' intention to use such a health recommendation system and that these effects are mediated by perceptions of effort, privacy risk, usefulness and enjoyment. Also, we find that consumers value usefulness of a system more and enjoyment less when a general practitioner advices them to use a health recommendation system than if they use it out of their own curiosity. Conclusions We developed and tested a model of consumers' intention to use a health recommendation system. We found that intermediaries play an important role in how consumers evaluate such a system over and above options of the underlying system that is used to generate the recommendation. Also, health-related information services seem to

Consumers' intention to use health recommendation systems to receive personalized nutrition advice.

PubMed

Wendel, Sonja; Dellaert, Benedict G C; Ronteltap, Amber; van Trijp, Hans C M

2013-04-04

Sophisticated recommendation systems are used more and more in the health sector to assist consumers in healthy decision making. In this study we investigate consumers' evaluation of hypothetical health recommendation systems that provide personalized nutrition advice. We examine consumers' intention to use such a health recommendation system as a function of options related to the underlying system (e.g. the type of company that generates the advice) as well as intermediaries (e.g. general practitioner) that might assist in using the system. We further explore if the effect of both the system and intermediaries on intention to use a health recommendation system are mediated by consumers' perceived effort, privacy risk, usefulness and enjoyment. 204 respondents from a consumer panel in the Netherlands participated. The data were collected by means of a questionnaire. Each respondent evaluated three hypothetical health recommendation systems on validated multi-scale measures of effort, privacy risk, usefulness, enjoyment and intention to use the system. To test the hypothesized relationships we used regression analyses. We find evidence that the options related to the underlying system as well as the intermediaries involved influence consumers' intention to use such a health recommendation system and that these effects are mediated by perceptions of effort, privacy risk, usefulness and enjoyment. Also, we find that consumers value usefulness of a system more and enjoyment less when a general practitioner advices them to use a health recommendation system than if they use it out of their own curiosity. We developed and tested a model of consumers' intention to use a health recommendation system. We found that intermediaries play an important role in how consumers evaluate such a system over and above options of the underlying system that is used to generate the recommendation. Also, health-related information services seem to rely on endorsement by the medical sector

A Holistic Approach to Networked Information Systems Design and Analysis

DTIC Science & Technology

2016-04-15

attain quite substantial savings. 11. Optimal algorithms for energy harvesting in wireless networks. We use a Markov- decision-process (MDP) based...approach to obtain optimal policies for transmissions . The key advantage of our approach is that it holistically considers information and energy in a...Coding technique to minimize delays and the number of transmissions in Wireless Systems. As we approach an era of ubiquitous computing with information

Collaborative filtering recommendation model based on fuzzy clustering algorithm

NASA Astrophysics Data System (ADS)

Yang, Ye; Zhang, Yunhua

2018-05-01

As one of the most widely used algorithms in recommender systems, collaborative filtering algorithm faces two serious problems, which are the sparsity of data and poor recommendation effect in big data environment. In traditional clustering analysis, the object is strictly divided into several classes and the boundary of this division is very clear. However, for most objects in real life, there is no strict definition of their forms and attributes of their class. Concerning the problems above, this paper proposes to improve the traditional collaborative filtering model through the hybrid optimization of implicit semantic algorithm and fuzzy clustering algorithm, meanwhile, cooperating with collaborative filtering algorithm. In this paper, the fuzzy clustering algorithm is introduced to fuzzy clustering the information of project attribute, which makes the project belong to different project categories with different membership degrees, and increases the density of data, effectively reduces the sparsity of data, and solves the problem of low accuracy which is resulted from the inaccuracy of similarity calculation. Finally, this paper carries out empirical analysis on the MovieLens dataset, and compares it with the traditional user-based collaborative filtering algorithm. The proposed algorithm has greatly improved the recommendation accuracy.

The Skills, Competences, and Attitude toward Information and Communications Technology Recommender System: an online support program for teachers with personalized recommendations

NASA Astrophysics Data System (ADS)

Revilla Muñoz, Olga; Alpiste Penalba, Francisco; Fernández Sánchez, Joaquín

2016-01-01

Teachers deal with Information and Communications Technology (ICT) every day and they often have to solve problems by themselves. To help them in coping with this issue, an online support program has been created, where teachers can pose their problems on ICT and they can receive solutions from other teachers. A Recommender System has been defined and implemented into the support program to suggest to each teacher the most suitable solution based on her Skills, Competences, and Attitude toward ICT (SCAT-ICT). The support program has initially been populated with 70 problems from 86 teachers. 30 teachers grouped these problems into six categories with the card-sorting technique. Real solutions to these problems have been proposed by 25 trained teachers. Finally, 17 teachers evaluated the usability of the support program and the Recommender System, where results showed a high score on the standardized System Usability Scale.

Using a Recommender System and Hyperwave Attributes To Augment an Electronic Resource Library.

ERIC Educational Resources Information Center

Fenn, B.; Lennon, J.

There has been increasing interest over the past few years in systems that help users exchange recommendations about World Wide Web documents. Programs have ranged from those that rely totally on user pre-selection, to others that are based on artificial intelligence. This paper proposes a system that falls between these two extremes, providing…

Auction and Game Theory Based Recommendations for DOD Acquisitions

DTIC Science & Technology

2015-03-24

SPONSORED REPORT SERIES Auction and Game Theory Based Recommendations for DOD Acquisitions 24 March 2015 Justin Blott, 2d Lt; Nicholas...TYPE 3. DATES COVERED 00-00-2015 to 00-00-2015 4. TITLE AND SUBTITLE Auction and Game Theory Based Recommendations for DOD Acquisitions 5a...Postgraduate School Abstract This paper synthesizes auction and game theory literature into specific military acquisition improvement

Recommended System Design for the Occupational Health Management Information System (OHMIS). Volume 1.

DTIC Science & Technology

1983-04-01

Management Information System (OHMIS). The system design includes: detailed function data flows for each of the core data processing functions of OHMIS, in the form of input/processing/output algorithms; detailed descriptions of the inputs and outputs; performance specifications of OHMIS; resources required to develop and operate OHMIS (Vol II). In addition, the report provides a summary of the rationale used to develop the recommended system design, a description of the methodology used to develop the recommended system design, and a review of existing

The Control Point Library Building System. [for Landsat MSS and RBV geometric image correction

NASA Technical Reports Server (NTRS)

Niblack, W.

1981-01-01

The Earth Resources Observation System (EROS) Data Center in Sioux Falls, South Dakota distributes precision corrected Landsat MSS and RBV data. These data are derived from master data tapes produced by the Master Data Processor (MDP), NASA's system for computing and applying corrections to the data. Included in the MDP is the Control Point Library Building System (CPLBS), an interactive, menu-driven system which permits a user to build and maintain libraries of control points. The control points are required to achieve the high geometric accuracy desired in the output MSS and RBV data. This paper describes the processing performed by CPLBS, the accuracy of the system, and the host computer and special image viewing equipment employed.

Evidence-Based Recommendations for Optimizing Light in Day-to-Day Spaceflight Operations

NASA Technical Reports Server (NTRS)

Whitmire, Alexandra; Leveton, Lauren; Barger, Laura; Clark, Toni; Bollweg, Laura; Ohnesorge, Kristine; Brainard, George

2015-01-01

NASA Behavioral Health and Performance Element (BHP) personnel have previously reported on efforts to transition evidence-based recommendations for a flexible lighting system on the International Space Station (ISS). Based on these recommendations, beginning in 2016 the ISS will replace the current fluorescent-based lights with an LED-based system to optimize visual performance, facilitate circadian alignment, promote sleep, and hasten schedule shifting. Additional efforts related to lighting countermeasures in spaceflight operations have also been underway. As an example, a recent BHP research study led by investigators at Harvard Medical School and Brigham and Women's Hospital, evaluated the acceptability, feasibility, and effectiveness of blue-enriched light exposure during exercise breaks for flight controllers working the overnight shift in the Mission Control Center (MCC) at NASA Johnson Space Center. This effort, along with published laboratory studies that have demonstrated the effectiveness of appropriately timed light for promoting alertness, served as an impetus for new light options, and educational protocols for flight controllers. In addition, a separate set of guidelines related to the light emitted from electronic devices, were provided to the Astronaut Office this past year. These guidelines were based on an assessment led by NASA's Lighting Environment Test Facility that included measuring the spectral power distribution, irradiance, and radiance of light emitted from ISS-grade laptops and I-Pads, as well as Android devices. Evaluations were conducted with and without the use of off-the-shelf screen filters as well as a software application that touts minimizing the short-wave length of the visible light spectrum. This presentation will focus on the transition for operations process related to lighting countermeasures in the MCC, as well as the evidence to support recommendations for optimal use of laptops, I-Pads, and Android devices during all

International and multidisciplinary expert recommendations for the use of biologics in systemic lupus erythematosus.

PubMed

Kleinmann, Jean-François; Tubach, Florence; Le Guern, Véronique; Mathian, Alexis; Richez, Christophe; Saadoun, David; Sacre, Karim; Sellam, Jérémie; Seror, Raphaèle; Amoura, Zahir; Andres, Emmanuel; Audia, Sylvain; Bader-Meunier, Brigitte; Blaison, Gilles; Bonnotte, Bernard; Cacoub, Patrice; Caillard, Sophie; Chiche, Laurent; Chosidow, Olivier; Costedoat-Chalumeau, Nathalie; Daien, Claire; Daugas, Eric; Derdèche, Nairouz; Doria, Andrea; Fain, Olivier; Fakhouri, Fadi; Farge, Dominique; Gabay, Cem; Guillo, Sylvie; Hachulla, Eric; Hajjaj-Hassouni, Najia; Hamidou, Mohamed; Houssiau, Frédéric A; Jourde-Chiche, Noémie; Koné-Paut, Isabelle; Ladjouz-Rezig, Aïcha; Lambotte, Olivier; Lipsker, Dan; Mariette, Xavier; Martin-Silva, Nicolas; Martin, Thierry; Maurier, François; Meckenstock, Roderich; Mékinian, Arsène; Meyer, Olivier; Mohamed, Shirine; Morel, Jacques; Moulin, Bruno; Mulleman, Denis; Papo, Thomas; Poindron, Vincent; Puéchal, Xavier; Punzi, Leonardo; Quartier, Pierre; Sailler, Laurent; Smail, Amar; Soubrier, Martin; Sparsa, Agnès; Tazi-Mezalek, Zoubida; Zakraoui, Leith; Zuily, Stéphane; Sibilia, Jean; Gottenberg, Jacques-Eric

2017-06-01

Despite conventional immunosuppressants, active and steroid-dependent systemic lupus erythematosus (SLE) represents a therapeutic challenge. Only one biologic, belimumab, has been approved, but other biologics are sometimes used off-label. Given the lack of evidence-based data in some clinical situations encountered in real life, we developed expert recommendations for the use of biologics for SLE. The recommendations were developed by a formal consensus method. This method aims to formalize the degree of agreement among experts by identifying, through iterative ratings with feedback, the points on which experts agree, disagree or are undecided. Hence, the recommendations are based on the agreed-upon points. We gathered the opinion of 59 French-speaking SLE experts from 3 clinical networks dedicated to systemic autoimmune diseases (FLEUR, IMIDIATE, FAI2R) from Algeria, Belgium, France, Italy, Morocco, Switzerland and Tunisia. Represented medical specialities were internal medicine (49%), rheumatology (34%), nephrology (7%), dermatology (5%), pediatrics (3%) and cardiology (2%). Two methodologists and 3 strictly independent SLE expert groups contributed to developing these recommendations: a steering group (SG) (n=9), an evaluation group (EG) (n=28) and a reading group (RG) (n=22). Preliminary recommendations were drafted by the SG, then proposed to the EG. Each EG member rated the degree of agreement from 1 to 9 (1: lowest; 9: strongest) for each recommendation. After 2 rating rounds, the SG submitted a new version of the recommendations to the RG. With comments from the RG, the SG finalised the recommendations. A total of 17 final recommendations were formulated by the SG, considering all agreement scores and comments by the EG and RG members and the two methodologists. These recommendations define the subset of patients who require a biologic; the type of biologics to use (belimumab, rituximab, etc.) depending on the organ involvement and associated co

Health Recommender Systems: Concepts, Requirements, Technical Basics and Challenges

PubMed Central

Wiesner, Martin; Pfeifer, Daniel

2014-01-01

During the last decades huge amounts of data have been collected in clinical databases representing patients' health states (e.g., as laboratory results, treatment plans, medical reports). Hence, digital information available for patient-oriented decision making has increased drastically but is often scattered across different sites. As as solution, personal health record systems (PHRS) are meant to centralize an individual's health data and to allow access for the owner as well as for authorized health professionals. Yet, expert-oriented language, complex interrelations of medical facts and information overload in general pose major obstacles for patients to understand their own record and to draw adequate conclusions. In this context, recommender systems may supply patients with additional laymen-friendly information helping to better comprehend their health status as represented by their record. However, such systems must be adapted to cope with the specific requirements in the health domain in order to deliver highly relevant information for patients. They are referred to as health recommender systems (HRS). In this article we give an introduction to health recommender systems and explain why they are a useful enhancement to PHR solutions. Basic concepts and scenarios are discussed and a first implementation is presented. In addition, we outline an evaluation approach for such a system, which is supported by medical experts. The construction of a test collection for case-related recommendations is described. Finally, challenges and open issues are discussed. PMID:24595212

Establishing Recommendations for Stroke Systems in the Thrombectomy Era: The Upstate New York Stakeholder Proceedings.

PubMed

Magdon-Ismail, Zainab; Benesch, Curtis; Cushman, Jeremy T; Brissette, Ian; Southerland, Andrew M; Brandler, Ethan S; Sozener, Cemal B; Flor, Sue; Hemmitt, Roseanne; Wales, Kathleen; Parrigan, Krystal; Levine, Steven R

2017-07-01

The American Heart Association/American Stroke Association and Department of Health Stroke Coverdell Program convened a stakeholder meeting in upstate NY to develop recommendations to enhance stroke systems for acute large vessel occlusion. Prehospital, hospital, and Department of Health leadership were invited (n=157). Participants provided goals/concerns and developed recommendations for prehospital triage and interfacility transport, rating each using a 3-level impact (A [high], B, and C [low]) and implementation feasibility (1 [high], 2, and 3 [low]) scale. Six weeks later, participants finalized recommendations. Seventy-one stakeholders (45% of invitees) attended. Six themes around goals/concerns emerged: (1) emergency medical services capacity, (2) validated prehospital screening tools, (3) facility capability, (4) triage/transport guidelines, (5) data capture/feedback tools, and (6) facility competition. In response, high-impact (level A) prehospital recommendations, stratified by implementation feasibility, were (1) use of online medical control for triage (6%); (2) regional transportation strategy (31%), standardized emergency medical services checklists (18%), quality metrics (14%), standardized prehospital screening tools (13%), and feedback for performance improvement (7%); and (3) smartphone application algorithm for screening/decision-making (6%) and ambulance-based telemedicine (6%). Level A interfacility transfer recommendations were (1) standardized transfer process (32%)/timing goals (16%)/regionalized systems (11%), performance metrics (11%), image sharing capabilities (7%); (2) provider education (9%) and stroke toolbox (5%); and (3) interfacility telemedicine (7%) and feedback (2%). The methods used and recommendations generated provide models for stroke system enhancement. Implementation may vary based on geographic need/capacity and be contingent on establishing standard care practices. Further research is needed to establish optimal

A hashtag recommendation system for twitter data streams.

PubMed

Otsuka, Eriko; Wallace, Scott A; Chiu, David

2016-01-01

Twitter has evolved into a powerful communication and information sharing tool used by millions of people around the world to post what is happening now. A hashtag, a keyword prefixed with a hash symbol (#), is a feature in Twitter to organize tweets and facilitate effective search among a massive volume of data. In this paper, we propose an automatic hashtag recommendation system that helps users find new hashtags related to their interests on-demand. For hashtag ranking, we propose the Hashtag Frequency-Inverse Hashtag Ubiquity (HF-IHU) ranking scheme, which is a variation of the well-known TF-IDF, that considers hashtag relevancy, as well as data sparseness which is one of the key challenges in analyzing microblog data. Our system is built on top of Hadoop, a leading platform for distributed computing, to provide scalable performance using Map-Reduce. Experiments on a large Twitter data set demonstrate that our method successfully yields relevant hashtags for user's interest and that recommendations are more stable and reliable than ranking tags based on tweet content similarity. Our results show that HF-IHU can achieve over 30 % hashtag recall when asked to identify the top 10 relevant hashtags for a particular tweet. Furthermore, our method out-performs kNN, k-popularity, and Naïve Bayes by 69, 54, and 17 %, respectively, on recall of the top 200 hashtags.

Effect of functional monomers in all-in-one adhesive systems on formation of enamel/dentin acid-base resistant zone.

PubMed

Nikaido, Toru; Ichikawa, Chiaki; Li, Na; Takagaki, Tomohiro; Sadr, Alireza; Yoshida, Yasuhiro; Suzuki, Kazuomi; Tagami, Junji

2011-01-01

This study aimed at evaluating the effect of functional monomers in all-in-one adhesive systems on formation of acid-base resistant zone (ABRZ) in enamel and dentin. Experimental adhesive systems containing one of three functional monomers; MDP, 3D-SR and 4-META were applied to enamel or dentin surface and light-cured. A universal resin composite was then placed. The specimens were subjected to a demineralizing solution (pH 4.5) and 5% NaClO for acid-base challenge and then observed by SEM. The ABRZ was clearly observed in both enamel and dentin interfaces. However, enamel ABRZ was thinner than dentin ABRZ in all adhesives. Morphology of the ABRZ was different between enamel and dentin, and also among the adhesives. Funnel-shaped erosion was observed only in the enamel specimen with the 4-META adhesive. The formation of enamel/dentin ABRZ was confirmed in all adhesives, but the morphology was influenced by the functional monomers.

GalenOWL: Ontology-based drug recommendations discovery

PubMed Central

2012-01-01

Background Identification of drug-drug and drug-diseases interactions can pose a difficult problem to cope with, as the increasingly large number of available drugs coupled with the ongoing research activities in the pharmaceutical domain, make the task of discovering relevant information difficult. Although international standards, such as the ICD-10 classification and the UNII registration, have been developed in order to enable efficient knowledge sharing, medical staff needs to be constantly updated in order to effectively discover drug interactions before prescription. The use of Semantic Web technologies has been proposed in earlier works, in order to tackle this problem. Results This work presents a semantic-enabled online service, named GalenOWL, capable of offering real time drug-drug and drug-diseases interaction discovery. For enabling this kind of service, medical information and terminology had to be translated to ontological terms and be appropriately coupled with medical knowledge of the field. International standards such as the aforementioned ICD-10 and UNII, provide the backbone of the common representation of medical data, while the medical knowledge of drug interactions is represented by a rule base which makes use of the aforementioned standards. Details of the system architecture are presented while also giving an outline of the difficulties that had to be overcome. A comparison of the developed ontology-based system with a similar system developed using a traditional business logic rule engine is performed, giving insights on the advantages and drawbacks of both implementations. Conclusions The use of Semantic Web technologies has been found to be a good match for developing drug recommendation systems. Ontologies can effectively encapsulate medical knowledge and rule-based reasoning can capture and encode the drug interactions knowledge. PMID:23256945

A proposed configurable approach for recommendation systems via data mining techniques

NASA Astrophysics Data System (ADS)

Khedr, Ayman E.; Idrees, Amira M.; Hegazy, Abd El-Fatah; El-Shewy, Samir

2018-02-01

This study presents a configurable approach for recommendations which determines the suitable recommendation method for each field based on the characteristics of its data, the method includes determining the suitable technique for selecting a representative sample of the provided data. Then selecting the suitable feature weighting measure to provide a correct weight for each feature based on its effect on the recommendations. Finally, selecting the suitable algorithm to provide the required recommendations. The proposed configurable approach could be applied on different domains. The experiments have revealed that the approach is able to provide recommendations with only 0.89 error rate percentage.

Contexts in a Paper Recommendation System with Collaborative Filtering

ERIC Educational Resources Information Center

Winoto, Pinata; Tang, Tiffany Ya; McCalla, Gordon

2012-01-01

Making personalized paper recommendations to users in an educational domain is not a trivial task of simply matching users' interests with a paper topic. Therefore, we proposed a context-aware multidimensional paper recommendation system that considers additional user and paper features. Earlier experiments on experienced graduate students…

A Recommendation on SLR Ranging to Future Global Navigation Satellite Systems

NASA Astrophysics Data System (ADS)

Labrecque, J. L.; Miller, J. J.; Pearlman, M.

2008-12-01

The multi-agency US Geodetic Requirements Working Group has recommended that Satellite Laser Retro- reflectors be installed on GPS III satellites as a principal component of the Positioning, Navigation, and Timing mandate of the Global Positioning System. The Working Group, which includes NASA, NGA, NOAA, NRL, USGS, and the USNO, echoes the Global Geodetic Observing System recommendation that SLR retro- reflectors be installed on all GNSS satellites. It is further recommended that the retro-reflectors conform to and hopefully exceed the minimum standard of the International Laser Ranging Service for retro-reflector cross sections of 100 million square meters for the HEO GNSS satellites to insure sufficiently accurate ranging by the global network of satellite laser ranging systems. The objective of this recommendation is to contribute to the improvement in the International Terrestrial Reference Frame, and its derivative the WGS84 reference frame, through continuing improvements in the characterization of the GPS orbits and clocks. Another objective is to provide an independent means of assessing the interoperability and accuracy of the GNSS systems and regional augmentation systems. The ranging to GNSS-mounted retro-reflectors will constitute a significant new means of space-based collocation to constrain the tie between the GPS and SLR networks that constitute over 50% of the data from which the ITRF is derived. The recommendation for the installation of SLR retro-reflectors aboard future GPS satellites is one of a number of efforts aimed at improving the accuracy and stability of ITRF. These steps are being coordinated with and supportive of the efforts of the GGOS and its services such at the VLBI2010 initiative, developing a next generation geodetic network, near real-time GPS positioning and EOP determination, and numerous efforts in the improvement of geodetic algorithms for GPS, SLR, VLBI, DORIS, and the determination of the ITRF. If past is prologue, the

Emergence of Scale-Free Leadership Structure in Social Recommender Systems

PubMed Central

Zhou, Tao; Medo, Matúš; Cimini, Giulio; Zhang, Zi-Ke; Zhang, Yi-Cheng

2011-01-01

The study of the organization of social networks is important for the understanding of opinion formation, rumor spreading, and the emergence of trends and fashion. This paper reports empirical analysis of networks extracted from four leading sites with social functionality (Delicious, Flickr, Twitter and YouTube) and shows that they all display a scale-free leadership structure. To reproduce this feature, we propose an adaptive network model driven by social recommending. Artificial agent-based simulations of this model highlight a “good get richer” mechanism where users with broad interests and good judgments are likely to become popular leaders for the others. Simulations also indicate that the studied social recommendation mechanism can gradually improve the user experience by adapting to tastes of its users. Finally we outline implications for real online resource-sharing systems. PMID:21857891

Personalized Recommendations Based on Users' Information-Centered Social Networks

ERIC Educational Resources Information Center

Lee, Danielle

2013-01-01

The overwhelming amount of information available today makes it difficult for users to find useful information and as the solution to this information glut problem, recommendation technologies emerged. Among the several streams of related research, one important evolution in technology is to generate recommendations based on users' own social…

TogoDoc server/client system: smart recommendation and efficient management of life science literature.

PubMed

Iwasaki, Wataru; Yamamoto, Yasunori; Takagi, Toshihisa

2010-12-13

In this paper, we describe a server/client literature management system specialized for the life science domain, the TogoDoc system (Togo, pronounced Toe-Go, is a romanization of a Japanese word for integration). The server and the client program cooperate closely over the Internet to provide life scientists with an effective literature recommendation service and efficient literature management. The content-based and personalized literature recommendation helps researchers to isolate interesting papers from the "tsunami" of literature, in which, on average, more than one biomedical paper is added to MEDLINE every minute. Because researchers these days need to cover updates of much wider topics to generate hypotheses using massive datasets obtained from public databases or omics experiments, the importance of having an effective literature recommendation service is rising. The automatic recommendation is based on the content of personal literature libraries of electronic PDF papers. The client program automatically analyzes these files, which are sometimes deeply buried in storage disks of researchers' personal computers. Just saving PDF papers to the designated folders makes the client program automatically analyze and retrieve metadata, rename file names, synchronize the data to the server, and receive the recommendation lists of newly published papers, thus accomplishing effortless literature management. In addition, the tag suggestion and associative search functions are provided for easy classification of and access to past papers (researchers who read many papers sometimes only vaguely remember or completely forget what they read in the past). The TogoDoc system is available for both Windows and Mac OS X and is free. The TogoDoc Client software is available at http://tdc.cb.k.u-tokyo.ac.jp/, and the TogoDoc server is available at https://docman.dbcls.jp/pubmed_recom.

TogoDoc Server/Client System: Smart Recommendation and Efficient Management of Life Science Literature

PubMed Central

Takagi, Toshihisa

2010-01-01

In this paper, we describe a server/client literature management system specialized for the life science domain, the TogoDoc system (Togo, pronounced Toe-Go, is a romanization of a Japanese word for integration). The server and the client program cooperate closely over the Internet to provide life scientists with an effective literature recommendation service and efficient literature management. The content-based and personalized literature recommendation helps researchers to isolate interesting papers from the “tsunami” of literature, in which, on average, more than one biomedical paper is added to MEDLINE every minute. Because researchers these days need to cover updates of much wider topics to generate hypotheses using massive datasets obtained from public databases or omics experiments, the importance of having an effective literature recommendation service is rising. The automatic recommendation is based on the content of personal literature libraries of electronic PDF papers. The client program automatically analyzes these files, which are sometimes deeply buried in storage disks of researchers' personal computers. Just saving PDF papers to the designated folders makes the client program automatically analyze and retrieve metadata, rename file names, synchronize the data to the server, and receive the recommendation lists of newly published papers, thus accomplishing effortless literature management. In addition, the tag suggestion and associative search functions are provided for easy classification of and access to past papers (researchers who read many papers sometimes only vaguely remember or completely forget what they read in the past). The TogoDoc system is available for both Windows and Mac OS X and is free. The TogoDoc Client software is available at http://tdc.cb.k.u-tokyo.ac.jp/, and the TogoDoc server is available at https://docman.dbcls.jp/pubmed_recom. PMID:21179453

SU-D-BRB-05: Quantum Learning for Knowledge-Based Response-Adaptive Radiotherapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

El Naqa, I; Ten, R

Purpose: There is tremendous excitement in radiotherapy about applying data-driven methods to develop personalized clinical decisions for real-time response-based adaptation. However, classical statistical learning methods lack in terms of efficiency and ability to predict outcomes under conditions of uncertainty and incomplete information. Therefore, we are investigating physics-inspired machine learning approaches by utilizing quantum principles for developing a robust framework to dynamically adapt treatments to individual patient’s characteristics and optimize outcomes. Methods: We studied 88 liver SBRT patients with 35 on non-adaptive and 53 on adaptive protocols. Adaptation was based on liver function using a split-course of 3+2 fractions with amore » month break. The radiotherapy environment was modeled as a Markov decision process (MDP) of baseline and one month into treatment states. The patient environment was modeled by a 5-variable state represented by patient’s clinical and dosimetric covariates. For comparison of classical and quantum learning methods, decision-making to adapt at one month was considered. The MDP objective was defined by the complication-free tumor control (P{sup +}=TCPx(1-NTCP)). A simple regression model represented state-action mapping. Single bit in classical MDP and a qubit of 2-superimposed states in quantum MDP represented the decision actions. Classical decision selection was done using reinforcement Q-learning and quantum searching was performed using Grover’s algorithm, which applies uniform superposition over possible states and yields quadratic speed-up. Results: Classical/quantum MDPs suggested adaptation (probability amplitude ≥0.5) 79% of the time for splitcourses and 100% for continuous-courses. However, the classical MDP had an average adaptation probability of 0.5±0.22 while the quantum algorithm reached 0.76±0.28. In cases where adaptation failed, classical MDP yielded 0.31±0.26 average amplitude while

Building a case-based diet recommendation system without a knowledge engineer.

PubMed

Khan, Abdus Salam; Hoffmann, Achim

2003-02-01

We present a new approach to the effective development of menu construction systems that allow to automatically construct a menu that is strongly tailored to the individual requirements and food preferences of a client. In hospitals and other health care institutions dietitians develop diets for clients which need to change their eating habits. Many clients have special needs in regards to their medical conditions, cultural backgrounds, or special levels of nutrient requirements for better recovery from diseases or surgery, etc. Existing computer support for this task is insufficient-many diets are not specifically tailored for the client's needs or require substantial time of a dietitian to be manually developed. Our approach is based on case-based reasoning, an artificial intelligence technique that finds increasing entry into industrial practice. Our approach goes beyond the traditional case-based reasoning (CBR) approach by allowing an incremental improvement of the system's competency during routine use of the system. The improvement of the system takes place through a direct expert user-system interaction while the expert is accomplishing their tasks of constructing a diet for a given client. Whenever the system performs unsatisfactorily, the expert will need to modify the system-produced diet 'manually', i.e. by entering the desired modifications into the system. Our implemented system, menu construction using an incremental knowledge acquisition system (MIKAS), asks the expert for simple explanations for each of the manual actions he/she takes and incorporates the explanations automatically into its knowledge base (KB) so that the system will perform these manually conducted actions automatically at the next occasion. We present MIKAS and discuss the results of our case study. While still being a prototype, the senior clinical dietitian involved in our evaluation studies judges the approach to have considerable potential to improve the daily routine of

Consensus-based recommendations for the management of juvenile dermatomyositis

PubMed Central

Enders, Felicitas Bellutti; Bader-Meunier, Brigitte; Baildam, Eileen; Constantin, Tamas; Dolezalova, Pavla; Feldman, Brian M; Lahdenne, Pekka; Magnusson, Bo; Nistala, Kiran; Ozen, Seza; Pilkington, Clarissa; Ravelli, Angelo; Russo, Ricardo; Uziel, Yosef; van Brussel, Marco; van der Net, Janjaap; Vastert, Sebastiaan; Wedderburn, Lucy R; Wulffraat, Nicolaas; McCann, Liza J; van Royen-Kerkhof, Annet

2017-01-01

Background In 2012, a European initiative called Single Hub and Access point for pediatric Rheumatology in Europe (SHARE) was launched to optimise and disseminate diagnostic and management regimens in Europe for children and young adults with rheumatic diseases. Juvenile dermatomyositis (JDM) is a rare disease within the group of paediatric rheumatic diseases (PRDs) and can lead to significant morbidity. Evidence-based guidelines are sparse and management is mostly based on physicians' experience. Consequently, treatment regimens differ throughout Europe. Objectives To provide recommendations for diagnosis and treatment of JDM. Methods Recommendations were developed by an evidence-informed consensus process using the European League Against Rheumatism standard operating procedures. A committee was constituted, consisting of 19 experienced paediatric rheumatologists and 2 experts in paediatric exercise physiology and physical therapy, mainly from Europe. Recommendations derived from a validated systematic literature review were evaluated by an online survey and subsequently discussed at two consensus meetings using nominal group technique. Recommendations were accepted if >80% agreement was reached. Results In total, 7 overarching principles, 33 recommendations on diagnosis and 19 recommendations on therapy were accepted with >80% agreement among experts. Topics covered include assessment of skin, muscle and major organ involvement and suggested treatment pathways. Conclusions The SHARE initiative aims to identify best practices for treatment of patients suffering from PRD. Within this remit, recommendations for the diagnosis and treatment of JDM have been formulated by an evidence-informed consensus process to produce a standard of care for patients with JDM throughout Europe. PMID:27515057

Personalized recommendation via unbalance full-connectivity inference

NASA Astrophysics Data System (ADS)

Ma, Wenping; Ren, Chen; Wu, Yue; Wang, Shanfeng; Feng, Xiang

2017-10-01

Recommender systems play an important role to help us to find useful information. They are widely used by most e-commerce web sites to push the potential items to individual user according to purchase history. Network-based recommendation algorithms are popular and effective in recommendation, which use two types of elements to represent users and items respectively. In this paper, based on consistence-based inference (CBI) algorithm, we propose a novel network-based algorithm, in which users and items are recognized with no difference. The proposed algorithm also uses information diffusion to find the relationship between users and items. Different from traditional network-based recommendation algorithms, information diffusion initializes from users and items, respectively. Experiments show that the proposed algorithm is effective compared with traditional network-based recommendation algorithms.

ICT Competence-Based Learning Object Recommendations for Teachers

ERIC Educational Resources Information Center

Sergis, Stylianos; Zervas, Panagiotis; Sampson, Demetrios G.

2014-01-01

Recommender Systems (RS) have been applied in the Technology enhanced Learning (TeL) field for facilitating, among others, Learning Object (LO) selection and retrieval. Most of the existing approaches, however, aim at accommodating the needs of learners and teacher-oriented RS are still an under-investigated field. Moreover, the systems that focus…

Adherence of preventive oral care products in the Syrian market to evidence-based international recommendations.

PubMed

Habes, D; Mahzia, R; Nakhleh, K; Joury, E

2016-09-25

No study has investigated the availability and adherence of preventive oral care products on the Syrian market to evidence-based international recommendations. Data were collected in 2012, and updated in 2016, in terms of availability, characteristics and adherence to evidence-based international recommendations. Few preventive products adhered to the recommendations. Despite the large decrease in the number of oral care products on the Syrian market, due to the Syrian crisis, nonadherence of some of the available products is still present. A multisectorial approach at a policy level is needed to address such important limitations. The Syrian Ministry of Health should reform regulations for fluoride products to become subject to drug monitoring systems; the Syrian Arab Committee for Measurements and Standards needs to update its standards; and the Syrian General Dental Association should distribute a preventive booklet to dental practitioners.

A novel framework to alleviate the sparsity problem in context-aware recommender systems

NASA Astrophysics Data System (ADS)

Yu, Penghua; Lin, Lanfen; Wang, Jing

2017-04-01

Recommender systems have become indispensable for services in the era of big data. To improve accuracy and satisfaction, context-aware recommender systems (CARSs) attempt to incorporate contextual information into recommendations. Typically, valid and influential contexts are determined in advance by domain experts or feature selection approaches. Most studies have focused on utilizing the unitary context due to the differences between various contexts. Meanwhile, multi-dimensional contexts will aggravate the sparsity problem, which means that the user preference matrix would become extremely sparse. Consequently, there are not enough or even no preferences in most multi-dimensional conditions. In this paper, we propose a novel framework to alleviate the sparsity issue for CARSs, especially when multi-dimensional contextual variables are adopted. Motivated by the intuition that the overall preferences tend to show similarities among specific groups of users and conditions, we first explore to construct one contextual profile for each contextual condition. In order to further identify those user and context subgroups automatically and simultaneously, we apply a co-clustering algorithm. Furthermore, we expand user preferences in a given contextual condition with the identified user and context clusters. Finally, we perform recommendations based on expanded preferences. Extensive experiments demonstrate the effectiveness of the proposed framework.

Consensus-based recommendations for the management of juvenile dermatomyositis.

PubMed

Enders, Felicitas Bellutti; Bader-Meunier, Brigitte; Baildam, Eileen; Constantin, Tamas; Dolezalova, Pavla; Feldman, Brian M; Lahdenne, Pekka; Magnusson, Bo; Nistala, Kiran; Ozen, Seza; Pilkington, Clarissa; Ravelli, Angelo; Russo, Ricardo; Uziel, Yosef; van Brussel, Marco; van der Net, Janjaap; Vastert, Sebastiaan; Wedderburn, Lucy R; Wulffraat, Nicolaas; McCann, Liza J; van Royen-Kerkhof, Annet

2017-02-01

In 2012, a European initiative called Single Hub and Access point for pediatric Rheumatology in Europe (SHARE) was launched to optimise and disseminate diagnostic and management regimens in Europe for children and young adults with rheumatic diseases. Juvenile dermatomyositis (JDM) is a rare disease within the group of paediatric rheumatic diseases (PRDs) and can lead to significant morbidity. Evidence-based guidelines are sparse and management is mostly based on physicians' experience. Consequently, treatment regimens differ throughout Europe. To provide recommendations for diagnosis and treatment of JDM. Recommendations were developed by an evidence-informed consensus process using the European League Against Rheumatism standard operating procedures. A committee was constituted, consisting of 19 experienced paediatric rheumatologists and 2 experts in paediatric exercise physiology and physical therapy, mainly from Europe. Recommendations derived from a validated systematic literature review were evaluated by an online survey and subsequently discussed at two consensus meetings using nominal group technique. Recommendations were accepted if >80% agreement was reached. In total, 7 overarching principles, 33 recommendations on diagnosis and 19 recommendations on therapy were accepted with >80% agreement among experts. Topics covered include assessment of skin, muscle and major organ involvement and suggested treatment pathways. The SHARE initiative aims to identify best practices for treatment of patients suffering from PRD. Within this remit, recommendations for the diagnosis and treatment of JDM have been formulated by an evidence-informed consensus process to produce a standard of care for patients with JDM throughout Europe. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Implementation of recommended trauma system criteria in south-eastern Norway: a cross-sectional hospital survey.

PubMed

Kristiansen, Thomas; Ringdal, Kjetil G; Skotheimsvik, Tarjei; Salthammer, Halvor K; Gaarder, Christine; Naess, Pål A; Lossius, Hans M

2012-01-26

Formalized trauma systems have shown beneficial effects on patient survival and have harvested great recognition among health care professionals. In spite of this, the implementation of trauma systems is challenging and often met with resistance.Recommendations for a national trauma system in Norway were published in 2007. We wanted to assess the level of implementation of these recommendations. A survey of all acute care hospitals that receive severely injured patients in the south-eastern health region of Norway was conducted. A structured questionnaire based on the 2007 national recommendations was used in a telephone interview of hospital trauma personnel between January 17 and 21, 2011. Seventeen trauma system criteria were identified from the recommendations. Nineteen hospitals were included in the study and these received more than 2000 trauma patients annually via their trauma teams. Out of the 17 criteria that had been identified, the hospitals fulfilled a median of 12 criteria. Neither the size of the hospitals nor the distance between the hospitals and the regional trauma centre affected the level of trauma resources available. The hospitals scored lowest on the criteria for transfer of patients to higher level of care and on the training requirements for members of the trauma teams. Our study identifies a major shortcoming in the efforts of regionalizing trauma in our region. The findings indicate that training of personnel and protocols for inter-hospital transfer are the major deficiencies from the national trauma system recommendations. Resources for training of personnel partaking in trauma teams and development of inter-hospital transfer agreements should receive immediate attention.

Network Location-Aware Service Recommendation with Random Walk in Cyber-Physical Systems

PubMed Central

Yin, Yuyu; Yu, Fangzheng; Xu, Yueshen; Yu, Lifeng; Mu, Jinglong

2017-01-01

Cyber-physical systems (CPS) have received much attention from both academia and industry. An increasing number of functions in CPS are provided in the way of services, which gives rise to an urgent task, that is, how to recommend the suitable services in a huge number of available services in CPS. In traditional service recommendation, collaborative filtering (CF) has been studied in academia, and used in industry. However, there exist several defects that limit the application of CF-based methods in CPS. One is that under the case of high data sparsity, CF-based methods are likely to generate inaccurate prediction results. In this paper, we discover that mining the potential similarity relations among users or services in CPS is really helpful to improve the prediction accuracy. Besides, most of traditional CF-based methods are only capable of using the service invocation records, but ignore the context information, such as network location, which is a typical context in CPS. In this paper, we propose a novel service recommendation method for CPS, which utilizes network location as context information and contains three prediction models using random walking. We conduct sufficient experiments on two real-world datasets, and the results demonstrate the effectiveness of our proposed methods and verify that the network location is indeed useful in QoS prediction. PMID:28885602

Network Location-Aware Service Recommendation with Random Walk in Cyber-Physical Systems.

PubMed

Yin, Yuyu; Yu, Fangzheng; Xu, Yueshen; Yu, Lifeng; Mu, Jinglong

2017-09-08

Cyber-physical systems (CPS) have received much attention from both academia and industry. An increasing number of functions in CPS are provided in the way of services, which gives rise to an urgent task, that is, how to recommend the suitable services in a huge number of available services in CPS. In traditional service recommendation, collaborative filtering (CF) has been studied in academia, and used in industry. However, there exist several defects that limit the application of CF-based methods in CPS. One is that under the case of high data sparsity, CF-based methods are likely to generate inaccurate prediction results. In this paper, we discover that mining the potential similarity relations among users or services in CPS is really helpful to improve the prediction accuracy. Besides, most of traditional CF-based methods are only capable of using the service invocation records, but ignore the context information, such as network location, which is a typical context in CPS. In this paper, we propose a novel service recommendation method for CPS, which utilizes network location as context information and contains three prediction models using random walking. We conduct sufficient experiments on two real-world datasets, and the results demonstrate the effectiveness of our proposed methods and verify that the network location is indeed useful in QoS prediction.

A Conceptual Framework for Evolving, Recommender Online Learning Systems

ERIC Educational Resources Information Center

Peiris, K. Dharini Amitha; Gallupe, R. Brent

2012-01-01

A comprehensive conceptual framework is developed and described for evolving recommender-driven online learning systems (ROLS). This framework describes how such systems can support students, course authors, course instructors, systems administrators, and policy makers in developing and using these ROLS. The design science information systems…

International recommendations for national patient safety incident reporting systems: an expert Delphi consensus-building process.

PubMed

Howell, Ann-Marie; Burns, Elaine M; Hull, Louise; Mayer, Erik; Sevdalis, Nick; Darzi, Ara

2017-02-01

Patient safety incident reporting systems (PSRS) have been established for over a decade, but uncertainty remains regarding the role that they can and ought to play in quantifying healthcare-related harm and improving care. To establish international, expert consensus on the purpose of PSRS regarding monitoring and learning from incidents and developing recommendations for their future role. After a scoping review of the literature, semi-structured interviews with experts in PSRS were conducted. Based on these findings, a survey-based questionnaire was developed and subsequently completed by a larger expert panel. Using a Delphi approach, consensus was reached regarding the ideal role of PSRSs. Recommendations for best practice were devised. Forty recommendations emerged from the Delphi procedure on the role and use of PSRS. Experts agreed reporting system should not be used as an epidemiological tool to monitor the rate of harm over time or to appraise the relative safety of hospitals. They agreed reporting is a valuable mechanism for identifying organisational safety needs. The benefit of a national system was clear with respect to medication error, device failures, hospital-acquired infections and never events as these problems often require solutions at a national level. Experts recommended training for senior healthcare professionals in incident investigation. Consensus recommendation was for hospitals to take responsibility for creating safety solutions locally that could be shared nationally. We obtained reasonable consensus among experts on aims and specifications of PSRS. This information can be used to reflect on existing and future PSRS, and their role within the wider patient safety landscape. The role of PSRS as instruments for learning needs to be elaborated and developed further internationally. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Altered Mental Status: Current Evidence-based Recommendations for Prehospital Care.

PubMed

Sanello, Ashley; Gausche-Hill, Marianne; Mulkerin, William; Sporer, Karl A; Brown, John F; Koenig, Kristi L; Rudnick, Eric M; Salvucci, Angelo A; Gilbert, Gregory H

2018-05-01

In the United States emergency medical services (EMS) protocols vary widely across jurisdictions. We sought to develop evidence-based recommendations for the prehospital evaluation and treatment of a patient with an acute change in mental status and to compare these recommendations against the current protocols used by the 33 EMS agencies in the State of California. We performed a literature review of the current evidence in the prehospital treatment of a patient with altered mental status (AMS) and augmented this review with guidelines from various national and international societies to create our evidence-based recommendations. We then compared the AMS protocols of each of the 33 EMS agencies for consistency with these recommendations. The specific protocol components that we analyzed were patient assessment, point-of-care tests, supplemental oxygen, use of standardized scoring, evaluating for causes of AMS, blood glucose evaluation, toxicological treatment, and pediatric evaluation and management. Protocols across 33 EMS agencies in California varied widely. All protocols call for a blood glucose check, 21 (64%) suggest treating adults at <60mg/dL, and half allow for the use of dextrose 10%. All the protocols recommend naloxone for signs of opioid overdose, but only 13 (39%) give specific parameters. Half the agencies (52%) recommend considering other toxicological causes of AMS, often by using the mnemonic AEIOU TIPS. Eight (24%) recommend a 12-lead electrocardiogram; others simply suggest cardiac monitoring. Fourteen (42%) advise supplemental oxygen as needed; only seven (21%) give specific parameters. In terms of considering various etiologies of AMS, 25 (76%) give instructions to consider trauma, 20 (61%) to consider stroke, and 18 (55%) to consider seizure. Twenty-three (70%) of the agencies have separate pediatric AMS protocols; others include pediatric considerations within the adult protocol. Protocols for patients with AMS vary widely across the State

Altered Mental Status: Current Evidence-based Recommendations for Prehospital Care

PubMed Central

Sanello, Ashley; Mulkerin, William; Sporer, Karl A.; Brown, John F.; Koenig, Kristi L.; Rudnick, Eric M.; Salvucci, Angelo A.; Gilbert, Gregory H.

2018-01-01

Introduction In the United States emergency medical services (EMS) protocols vary widely across jurisdictions. We sought to develop evidence-based recommendations for the prehospital evaluation and treatment of a patient with an acute change in mental status and to compare these recommendations against the current protocols used by the 33 EMS agencies in the State of California. Methods We performed a literature review of the current evidence in the prehospital treatment of a patient with altered mental status (AMS) and augmented this review with guidelines from various national and international societies to create our evidence-based recommendations. We then compared the AMS protocols of each of the 33 EMS agencies for consistency with these recommendations. The specific protocol components that we analyzed were patient assessment, point-of-care tests, supplemental oxygen, use of standardized scoring, evaluating for causes of AMS, blood glucose evaluation, toxicological treatment, and pediatric evaluation and management. Results Protocols across 33 EMS agencies in California varied widely. All protocols call for a blood glucose check, 21 (64%) suggest treating adults at <60mg/dL, and half allow for the use of dextrose 10%. All the protocols recommend naloxone for signs of opioid overdose, but only 13 (39%) give specific parameters. Half the agencies (52%) recommend considering other toxicological causes of AMS, often by using the mnemonic AEIOU TIPS. Eight (24%) recommend a 12-lead electrocardiogram; others simply suggest cardiac monitoring. Fourteen (42%) advise supplemental oxygen as needed; only seven (21%) give specific parameters. In terms of considering various etiologies of AMS, 25 (76%) give instructions to consider trauma, 20 (61%) to consider stroke, and 18 (55%) to consider seizure. Twenty-three (70%) of the agencies have separate pediatric AMS protocols; others include pediatric considerations within the adult protocol. Conclusion Protocols for

A Novel Recommendation System to Match College Events and Groups to Students

NASA Astrophysics Data System (ADS)

Qazanfari, K.; Youssef, A.; Keane, K.; Nelson, J.

2017-10-01

With the recent increase in data online, discovering meaningful opportunities can be time-consuming and complicated for many individuals. To overcome this data overload challenge, we present a novel text-content-based recommender system as a valuable tool to predict user interests. To that end, we develop a specific procedure to create user models and item feature-vectors, where items are described in free text. The user model is generated by soliciting from a user a few keywords and expanding those keywords into a list of weighted near-synonyms. The item feature-vectors are generated from the textual descriptions of the items, using modified tf-idf values of the users’ keywords and their near-synonyms. Once the users are modeled and the items are abstracted into feature vectors, the system returns the maximum-similarity items as recommendations to that user. Our experimental evaluation shows that our method of creating the user models and item feature-vectors resulted in higher precision and accuracy in comparison to well-known feature-vector-generating methods like Glove and Word2Vec. It also shows that stemming and the use of a modified version of tf-idf increase the accuracy and precision by 2% and 3%, respectively, compared to non-stemming and the standard tf-idf definition. Moreover, the evaluation results show that updating the user model from usage histories improves the precision and accuracy of the system. This recommender system has been developed as part of the Agnes application, which runs on iOS and Android platforms and is accessible through the Agnes website.

Mining Feedback in Ranking and Recommendation Systems

ERIC Educational Resources Information Center

Zhuang, Ziming

2009-01-01

The amount of online information has grown exponentially over the past few decades, and users become more and more dependent on ranking and recommendation systems to address their information seeking needs. The advance in information technologies has enabled users to provide feedback on the utilities of the underlying ranking and recommendation…

A Dynamic Recommender System for Improved Web Usage Mining and CRM Using Swarm Intelligence.

PubMed

Alphy, Anna; Prabakaran, S

2015-01-01

In modern days, to enrich e-business, the websites are personalized for each user by understanding their interests and behavior. The main challenges of online usage data are information overload and their dynamic nature. In this paper, to address these issues, a WebBluegillRecom-annealing dynamic recommender system that uses web usage mining techniques in tandem with software agents developed for providing dynamic recommendations to users that can be used for customizing a website is proposed. The proposed WebBluegillRecom-annealing dynamic recommender uses swarm intelligence from the foraging behavior of a bluegill fish. It overcomes the information overload by handling dynamic behaviors of users. Our dynamic recommender system was compared against traditional collaborative filtering systems. The results show that the proposed system has higher precision, coverage, F1 measure, and scalability than the traditional collaborative filtering systems. Moreover, the recommendations given by our system overcome the overspecialization problem by including variety in recommendations.

A Dynamic Recommender System for Improved Web Usage Mining and CRM Using Swarm Intelligence

PubMed Central

Alphy, Anna; Prabakaran, S.

2015-01-01

In modern days, to enrich e-business, the websites are personalized for each user by understanding their interests and behavior. The main challenges of online usage data are information overload and their dynamic nature. In this paper, to address these issues, a WebBluegillRecom-annealing dynamic recommender system that uses web usage mining techniques in tandem with software agents developed for providing dynamic recommendations to users that can be used for customizing a website is proposed. The proposed WebBluegillRecom-annealing dynamic recommender uses swarm intelligence from the foraging behavior of a bluegill fish. It overcomes the information overload by handling dynamic behaviors of users. Our dynamic recommender system was compared against traditional collaborative filtering systems. The results show that the proposed system has higher precision, coverage, F1 measure, and scalability than the traditional collaborative filtering systems. Moreover, the recommendations given by our system overcome the overspecialization problem by including variety in recommendations. PMID:26229978

Implementation of recommended trauma system criteria in south-eastern Norway: a cross-sectional hospital survey

PubMed Central

2012-01-01

Background Formalized trauma systems have shown beneficial effects on patient survival and have harvested great recognition among health care professionals. In spite of this, the implementation of trauma systems is challenging and often met with resistance. Recommendations for a national trauma system in Norway were published in 2007. We wanted to assess the level of implementation of these recommendations. Methods A survey of all acute care hospitals that receive severely injured patients in the south-eastern health region of Norway was conducted. A structured questionnaire based on the 2007 national recommendations was used in a telephone interview of hospital trauma personnel between January 17 and 21, 2011. Seventeen trauma system criteria were identified from the recommendations. Results Nineteen hospitals were included in the study and these received more than 2000 trauma patients annually via their trauma teams. Out of the 17 criteria that had been identified, the hospitals fulfilled a median of 12 criteria. Neither the size of the hospitals nor the distance between the hospitals and the regional trauma centre affected the level of trauma resources available. The hospitals scored lowest on the criteria for transfer of patients to higher level of care and on the training requirements for members of the trauma teams. Conclusion Our study identifies a major shortcoming in the efforts of regionalizing trauma in our region. The findings indicate that training of personnel and protocols for inter-hospital transfer are the major deficiencies from the national trauma system recommendations. Resources for training of personnel partaking in trauma teams and development of inter-hospital transfer agreements should receive immediate attention. PMID:22281020

Exploring Learner Attitudes toward Web-Based Recommendation Learning Service System for Interdisciplinary Applications

ERIC Educational Resources Information Center

Chen, Hong-Ren; Huang, Jhen-Gang

2012-01-01

The booming digital-content industry has resulted in an increasing number of e-learning Internet websites that provide online learning services. Recommendations for learning sites are used by diverse learners to identify the most appropriate learning resources. However, research into recommendations about learning has concentrated primarily on…

One to One Recommendation System in Apparel On-Line Shopping

NASA Astrophysics Data System (ADS)

Sekozawa, Teruji; Mitsuhashi, Hiroyuki; Ozawa, Yukio

We propose an apparel online shopping site that the fashion adviser exists on the internet. The fashion adviser, who has detailed knowledge about the fashion in real shop, selects and coordinates the clothes of the customer's preference. However, the customer, who didn't have detailed knowledge about the fashion, was not able to choose the clothes suitable for the customer's preference from among the candidate of a large amount of clothes on a conventional apparel shopping site. Then, we compose the system that analyzes the customer's preference by the AHP technique, makes to the cluster by the correlation of clothes, and analyzes the market basket. As a result, this system can coordinate the clothes appropriate for the favor of an individual customer. Moreover, this system can propose the recommendation of other clothes based on past sales data.

A fast combination method in DSmT and its application to recommender system

PubMed Central

Liu, Yihai

2018-01-01

In many applications involving epistemic uncertainties usually modeled by belief functions, it is often necessary to approximate general (non-Bayesian) basic belief assignments (BBAs) to subjective probabilities (called Bayesian BBAs). This necessity occurs if one needs to embed the fusion result in a system based on the probabilistic framework and Bayesian inference (e.g. tracking systems), or if one needs to make a decision in the decision making problems. In this paper, we present a new fast combination method, called modified rigid coarsening (MRC), to obtain the final Bayesian BBAs based on hierarchical decomposition (coarsening) of the frame of discernment. Regarding this method, focal elements with probabilities are coarsened efficiently to reduce computational complexity in the process of combination by using disagreement vector and a simple dichotomous approach. In order to prove the practicality of our approach, this new approach is applied to combine users’ soft preferences in recommender systems (RSs). Additionally, in order to make a comprehensive performance comparison, the proportional conflict redistribution rule #6 (PCR6) is regarded as a baseline in a range of experiments. According to the results of experiments, MRC is more effective in accuracy of recommendations compared to original Rigid Coarsening (RC) method and comparable in computational time. PMID:29351297

Comparing a Mobile Decision Support System Versus the Use of Printed Materials for the Implementation of an Evidence-Based Recommendation: Protocol for a Qualitative Evaluation.

PubMed

Camacho, Jhon; Medina Ch, Ana María; Landis-Lewis, Zach; Douglas, Gerald; Boyce, Richard

2018-04-13

The distribution of printed materials is the most frequently used strategy to disseminate and implement clinical practice guidelines, although several studies have shown that the effectiveness of this approach is modest at best. Nevertheless, there is insufficient evidence to support the use of other strategies. Recent research has shown that the use of computerized decision support presents a promising approach to address some aspects of this problem. The aim of this study is to provide qualitative evidence on the potential effect of mobile decision support systems to facilitate the implementation of evidence-based recommendations included in clinical practice guidelines. We will conduct a qualitative study with two arms to compare the experience of primary care physicians while they try to implement an evidence-based recommendation in their clinical practice. In the first arm, we will provide participants with a printout of the guideline article containing the recommendation, while in the second arm, we will provide participants with a mobile app developed after formalizing the recommendation text into a clinical algorithm. Data will be collected using semistructured and open interviews to explore aspects of behavioral change and technology acceptance involved in the implementation process. The analysis will be comprised of two phases. During the first phase, we will conduct a template analysis to identify barriers and facilitators in each scenario. Then, during the second phase, we will contrast the findings from each arm to propose hypotheses about the potential impact of the system. We have formalized the narrative in the recommendation into a clinical algorithm and have developed a mobile app. Data collection is expected to occur during 2018, with the first phase of analysis running in parallel. The second phase is scheduled to conclude in July 2019. Our study will further the understanding of the role of mobile decision support systems in the implementation

Identifying problems and generating recommendations for enhancing complex systems: applying the abstraction hierarchy framework as an analytical tool.

PubMed

Xu, Wei

2007-12-01

This study adopts J. Rasmussen's (1985) abstraction hierarchy (AH) framework as an analytical tool to identify problems and pinpoint opportunities to enhance complex systems. The process of identifying problems and generating recommendations for complex systems using conventional methods is usually conducted based on incompletely defined work requirements. As the complexity of systems rises, the sheer mass of data generated from these methods becomes unwieldy to manage in a coherent, systematic form for analysis. There is little known work on adopting a broader perspective to fill these gaps. AH was used to analyze an aircraft-automation system in order to further identify breakdowns in pilot-automation interactions. Four steps follow: developing an AH model for the system, mapping the data generated by various methods onto the AH, identifying problems based on the mapped data, and presenting recommendations. The breakdowns lay primarily with automation operations that were more goal directed. Identified root causes include incomplete knowledge content and ineffective knowledge structure in pilots' mental models, lack of effective higher-order functional domain information displayed in the interface, and lack of sufficient automation procedures for pilots to effectively cope with unfamiliar situations. The AH is a valuable analytical tool to systematically identify problems and suggest opportunities for enhancing complex systems. It helps further examine the automation awareness problems and identify improvement areas from a work domain perspective. Applications include the identification of problems and generation of recommendations for complex systems as well as specific recommendations regarding pilot training, flight deck interfaces, and automation procedures.

Method and system of filtering and recommending documents

DOEpatents

Patton, Robert M.; Potok, Thomas E.

2016-02-09

Disclosed is a method and system for discovering documents using a computer and providing a small set of the most relevant documents to the attention of a human observer. Using the method, the computer obtains a seed document from the user and generates a seed document vector using term frequency-inverse corpus frequency weighting. A keyword index for a plurality of source documents can be compared with the weighted terms of the seed document vector. The comparison is then filtered to reduce the number of documents, which define an initial subset of the source documents. Initial subset vectors are generated and compared to the seed document vector to obtain a similarity value for each comparison. Based on the similarity value, the method then recommends one or more of the source documents.

Similarity-Based Recommendation of New Concepts to a Terminology

PubMed Central

Chandar, Praveen; Yaman, Anil; Hoxha, Julia; He, Zhe; Weng, Chunhua

2015-01-01

Terminologies can suffer from poor concept coverage due to delays in addition of new concepts. This study tests a similarity-based approach to recommending concepts from a text corpus to a terminology. Our approach involves extraction of candidate concepts from a given text corpus, which are represented using a set of features. The model learns the important features to characterize a concept and recommends new concepts to a terminology. Further, we propose a cost-effective evaluation methodology to estimate the effectiveness of terminology enrichment methods. To test our methodology, we use the clinical trial eligibility criteria free-text as an example text corpus to recommend concepts for SNOMED CT. We computed precision at various rank intervals to measure the performance of the methods. Results indicate that our automated algorithm is an effective method for concept recommendation. PMID:26958170

Landslide triggering thresholds for Switzerland based on a new gridded precipitation dataset

NASA Astrophysics Data System (ADS)

Leonarduzzi, Elena; Molnar, Peter; McArdell, Brian W.

2017-04-01

In Switzerland floods are responsible for most of the damage caused by rainfall-triggered natural hazards (89%), followed by landslides (6%, ca. 520 M Euros) as reported in Hilker et al. (2009) for the period 1972-2007. The prediction of landslide occurrence is particularly challenging because of their wide distribution in space and the complex interdependence of predisposing and triggering factors. The overall goal of our research is to develop an Early Warning System for landsliding in Switzerland based on hydrological modelling and rainfall forecasts. In order to achieve this, we first analyzed rainfall triggering thresholds for landslides from a new gridded daily precipitation dataset (RhiresD, MeteoSwiss) for Switzerland combined with landslide events recorded in the Swiss Damage Database (Hilker et al.,2009). The high-resolution gridded precipitation dataset allows us to collocate rainfall and landslides accurately in space, which is an advantage over many previous studies. Each of the 2272 landslides in the database in the period 1972-2012 was assigned to the corresponding 2x2 km precipitation cell. For each of these cells, precipitation events were defined as series of consecutive rainy days and the following event parameters were computed: duration (day), maximum and mean daily intensity (mm/day), total rainfall depth (mm) and maximum daily intensity divided by Mean Daily Precipitation (MDP). The events were classified as triggering or non-triggering depending on whether a landslide was recorded in the cell during the event. This classification of observations was compared to predictions based on a threshold for each of the parameters. The predictive power of each parameter and the best threshold value were quantified by ROC analysis and statistics such as AUC and the True Skill Statistic (TSS). Event parameters based on rainfall intensity were found to have similarly high predictive power (TSS=0.54-0.59, AUC=0.85-0.86), while rainfall duration had a

Antibiotics and Facial Fractures: Evidence-Based Recommendations Compared with Experience-Based Practice

PubMed Central

Mundinger, Gerhard S.; Borsuk, Daniel E.; Okhah, Zachary; Christy, Michael R.; Bojovic, Branko; Dorafshar, Amir H.; Rodriguez, Eduardo D.

2014-01-01

Efficacy of prophylactic antibiotics in craniofacial fracture management is controversial. The purpose of this study was to compare evidence-based literature recommendations regarding antibiotic prophylaxis in facial fracture management with expert-based practice. A systematic review of the literature was performed to identify published studies evaluating pre-, peri-, and postoperative efficacy of antibiotics in facial fracture management by facial third. Study level of evidence was assessed according to the American Society of Plastic Surgery criteria, and graded practice recommendations were made based on these assessments. Expert opinions were garnered during the Advanced Orbital Surgery Symposium in the form of surveys evaluating senior surgeon clinical antibiotic prescribing practices by time point and facial third. A total of 44 studies addressing antibiotic prophylaxis and facial fracture management were identified. Overall, studies were of poor quality, precluding formal quantitative analysis. Studies supported the use of perioperative antibiotics in all facial thirds, and preoperative antibiotics in comminuted mandible fractures. Postoperative antibiotics were not supported in any facial third. Survey respondents (n = 17) cumulatively reported their antibiotic prescribing practices over 286 practice years and 24,012 facial fracture cases. Percentages of prescribers administering pre-, intra-, and postoperative antibiotics, respectively, by facial third were as follows: upper face 47.1, 94.1, 70.6; midface 47.1, 100, 70.6%; and mandible 68.8, 94.1, 64.7%. Preoperative but not postoperative antibiotic use is recommended for comminuted mandible fractures. Frequent use of pre- and postoperative antibiotics in upper and midface fractures is not supported by literature recommendations, but with low-level evidence. Higher level studies may better guide clinical antibiotic prescribing practices. PMID:25709755

Personalized Recommendation of Learning Material Using Sequential Pattern Mining and Attribute Based Collaborative Filtering

ERIC Educational Resources Information Center

Salehi, Mojtaba; Nakhai Kamalabadi, Isa; Ghaznavi Ghoushchi, Mohammad Bagher

2014-01-01

Material recommender system is a significant part of e-learning systems for personalization and recommendation of appropriate materials to learners. However, in the existing recommendation algorithms, dynamic interests and multi-preference of learners and multidimensional-attribute of materials are not fully considered simultaneously. Moreover,…

Management of prolonged post-operative ileus: evidence-based recommendations.

PubMed

Vather, Ryash; Bissett, Ian

2013-05-01

Prolonged post-operative ileus (PPOI) occurs in up to 25% of patients following major elective abdominal surgery. It is associated with a higher risk of developing post-operative complications, prolongs hospital stay and confers a significant financial load on health-care institutions. Literature outlining best-practice management strategies for PPOI is nebulous. The aim of this text was to review the literature and provide concise evidence-based recommendations for its management. A literature search through the Ovid MEDLINE, EMBASE, Google Scholar and Cochrane databases was performed from inception to July 2012 using a combination of keywords and MeSH terms. Review of the literature was followed by synthesis of concise recommendations for management accompanied by Strength of Recommendation Taxonomy (either A, B or C). Recommendations for management include regular evaluation and correction of electrolytes (B); review of analgesic prescription with weaning of narcotics and substitution with regular paracetamol, regular non-steroidal anti-inflammatory drugs if not contraindicated, and regular or as-required Tramadol (A); nasogastric decompression for those with nausea or vomiting as prominent features (C); isotonic dextrose-saline crystalloid maintenance fluids administered within a restrictive regimen (B); balanced isotonic crystalloid replacement fluids containing supplemental potassium, in equivalent volume to losses (C); regular ambulation (C); parenteral nutrition if unable to tolerate an adequate oral intake for more than 7 days post-operatively (A) and exclusion of precipitating pathology or alternate diagnoses if clinically suspected (C). Recommendations have a variable and frequently inconsistent evidence base. Further research is required to validate many of the outlined recommendations and to investigate novel interventions that may be used to shorten duration of PPOI. © 2013 The Authors. ANZ Journal of Surgery © 2013 Royal Australasian College of

Hybrid recommendation methods in complex networks.

PubMed

Fiasconaro, A; Tumminello, M; Nicosia, V; Latora, V; Mantegna, R N

2015-07-01

We propose two recommendation methods, based on the appropriate normalization of already existing similarity measures, and on the convex combination of the recommendation scores derived from similarity between users and between objects. We validate the proposed measures on three data sets, and we compare the performance of our methods to other recommendation systems recently proposed in the literature. We show that the proposed similarity measures allow us to attain an improvement of performances of up to 20% with respect to existing nonparametric methods, and that the accuracy of a recommendation can vary widely from one specific bipartite network to another, which suggests that a careful choice of the most suitable method is highly relevant for an effective recommendation on a given system. Finally, we study how an increasing presence of random links in the network affects the recommendation scores, finding that one of the two recommendation algorithms introduced here can systematically outperform the others in noisy data sets.

SYNAISTHISI: an IoT-powered smart visitor management and cognitive recommendations system

NASA Astrophysics Data System (ADS)

Thanos, Giorgos Konstandinos; Karafylli, Christina; Karafylli, Maria; Zacharakis, Dimitris; Papadimitriou, Apostolis; Dimitros, Kostantinos; Kanellopoulou, Konstantina; Kyriazanos, Dimitris M.; Thomopoulos, Stelios C. A.

2016-05-01

Location-based and navigation services are really needed to help visitors and audience of big events, complex buildings, shopping malls, airports and large companies. However, the lack of GPS and proper mapping indoors usually renders location-based applications and services useless or simply not applicable in such environments. SYNAISTHISI introduces a mobile application for smartphones which offers navigation capabilities outside and inside buildings and through multiple floor levels. The application comes together with a suite of helpful services, including personalized recommendations, visit/event management and a helpful search functionality in order to navigate to a specific location, event or person. As the user finds his way towards his destination, NFC-enabled checkpoints and bluetooth beacons assist him, while offering re-routing, check-in/out capabilities and useful information about ongoing meetings and nearby events. The application is supported by a back-end GIS system which can provide a broad and clear view to event organizers, campus managers and field personnel for purposes of event logistics, safety and security. SYNAISTHISI system comes with plenty competitive advantages including (a) Seamless Navigation as users move between outdoor and indoor areas and different floor levels by using innovative routing algorithms, (b) connection to and powered by IoT platform, for localization and real-time information feedback, (c) dynamic personalized recommendations based on user profile, location and real-time information provided by the IoT platform and (d) Indoor localization without the need for expensive infrastructure and installations.

TaDb: A time-aware diffusion-based recommender algorithm

NASA Astrophysics Data System (ADS)

Li, Wen-Jun; Xu, Yuan-Yuan; Dong, Qiang; Zhou, Jun-Lin; Fu, Yan

2015-02-01

Traditional recommender algorithms usually employ the early and recent records indiscriminately, which overlooks the change of user interests over time. In this paper, we show that the interests of a user remain stable in a short-term interval and drift during a long-term period. Based on this observation, we propose a time-aware diffusion-based (TaDb) recommender algorithm, which assigns different temporal weights to the leading links existing before the target user's collection and the following links appearing after that in the diffusion process. Experiments on four real datasets, Netflix, MovieLens, FriendFeed and Delicious show that TaDb algorithm significantly improves the prediction accuracy compared with the algorithms not considering temporal effects.

Recommendations to enhance constructivist-based learning in Interprofessional Education using video-based self-assessment

PubMed Central

Dahmen, Uta; Schulze, Christine; Schindler, Claudia; Wick, Katharina; Schwartze, Dominique; Veit, Andrea; Smolenski, Ulrich

2016-01-01

Introduction: Interprofessional collaboration is crucial to the optimization of patient care. Aim: This paper aims to provide recommendations for implementing an innovative constructivist educational concept with the core element of video-based self-assessment. Methodology: A course for students in medicine, physiotherapy, and nursing was developed through interprofessional, cross-institutional collaboration. The course consisted of drawing on prior knowledge about the work done by each professional group in regard to a specific clinical scenario and an interprofessional treatment situation, filming a role play of this treatment situation, and a structured self-assessment of the role play. We evaluated the preparation and implementation of the three courses conducted thus far. Concrete recommendations for implementation were made based on evaluation sheets (students), open discussions (tutors, instructors, institutions) and recorded meeting minutes (project managers, project participants). Results: Basic recommendations for implementation include: selecting appropriate criteria for self-assessment and a simulated situation that offers members of each professional group an equal opportunity to act in the role play. In terms of administrative implementation we recommend early coordination among the professions and educational institutions regarding the target groups, scheduling and attendance policy to ensure participant recruitment across all professions. Procedural planning should include developing teaching materials, such as the case vignette and treatment scenario, and providing technical equipment that can be operated intuitively in order to ensure efficient recording. Conclusion: These recommendations serve as an aid for implementing an innovative constructivist educational concept with video-based self-assessment at its core. PMID:27280144

Designing Dietary Recommendations Using System Level Interactomics Analysis and Network-Based Inference

PubMed Central

Zheng, Tingting; Ni, Yueqiong; Li, Jun; Chow, Billy K. C.; Panagiotou, Gianni

2017-01-01

development. Due to the complexity of analyzing the food composition and eating patterns of individuals our in silico analysis, using large-scale gene expression datasets and network-based topological features, may serve as a proof-of-concept in nutritional systems biology for identifying diet-disease relationships and subsequently designing dietary recommendations. PMID:29033850

Expert Recommender: Designing for a Network Organization

NASA Astrophysics Data System (ADS)

Reichling, Tim; Veith, Michael; Wulf, Volker

Recent knowledge management initiatives focus on expertise sharing within formal organizational units and informal communities of practice. Expert recommender systems seem to be a promising tool in support of these initiatives. This paper presents experiences in designing an expert recommender system for a knowledge- intensive organization, namely the National Industry Association (NIA). Field study results provide a set of specific design requirements. Based on these requirements, we have designed an expert recommender system which is integrated into the specific software infrastructure of the organizational setting. The organizational setting is, as we will show, specific for historical, political, and economic reasons. These particularities influence the employees’ organizational and (inter-)personal needs within this setting. The paper connects empirical findings of a long-term case study with design experiences of an expertise recommender system.

Action Recommendation for Cyber Resilience

DOE Office of Scientific and Technical Information (OSTI.GOV)

Choudhury, Sutanay; Rodriguez, Luke R.; Curtis, Darren S.

2015-09-01

This paper presents an unifying graph-based model for representing the infrastructure, behavior and missions of an enterprise. We describe how the model can be used to achieve resiliency against a wide class of failures and attacks. We introduce an algorithm for recommending resilience establishing actions based on dynamic updates to the models. Without loss of generality, we show the effectiveness of the algorithm for preserving latency based quality of service (QoS). Our models and the recommendation algorithms are implemented in a software framework that we seek to release as an open source framework for simulating resilient cyber systems.

Answering medical questions at the point of care: a cross-sectional study comparing rapid decisions based on PubMed and Epistemonikos searches with evidence-based recommendations developed with the GRADE approach.

PubMed

Izcovich, Ariel; Criniti, Juan Martín; Popoff, Federico; Ragusa, Martín Alberto; Gigler, Cristel; Gonzalez Malla, Carlos; Clavijo, Manuela; Manzotti, Matias; Diaz, Martín; Catalano, Hugo Norberto; Neumann, Ignacio; Guyatt, Gordon

2017-08-07

Using the best current evidence to inform clinical decisions remains a challenge for clinicians. Given the scarcity of trustworthy clinical practice guidelines providing recommendations to answer clinicians' daily questions, clinical decision support systems (ie, assistance in question identification and answering) emerge as an attractive alternative. The trustworthiness of the recommendations achieved by such systems is unknown. To evaluate the trustworthiness of a question identification and answering system that delivers timely recommendations. Cross-sectional study. We compared the responses to 100 clinical questions related to inpatient management provided by two rapid response methods with 'Gold Standard' recommendations. One of the rapid methods was based on PubMed and the other on Epistemonikos database. We defined our 'Gold Standard' as trustworthy published evidence-based recommendations or, when unavailable, recommendations developed locally by a panel of six clinicians following the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. Recommendations provided by the rapid strategies were classified as potentially misleading or reasonable. We also determined if the potentially misleading recommendations could have been avoided with the appropriate implementation of searching and evidence summary tools. We were able to answer all of the 100 questions with both rapid methods. Of the 200 recommendations obtained, 6.5% (95% CI 3% to 9.9%) were classified as potentially misleading and 93.5% (95% CI 90% to 96.9%) as reasonable. 6 of the 13 potentially misleading recommendations could have been avoided by the appropriate usage of the Epistemonikos matrix tool or by constructing summary of findings tables. No significant differences were observed between the evaluated rapid response methods. A question answering service based on the GRADE approach proved feasible to implement and provided appropriate guidance for most identified

Application of Recommended Design Practices for Conceptual Nuclear Fusion Space Propulsion Systems

NASA Technical Reports Server (NTRS)

Williams, Craig H.

2004-01-01

An AIAA Special Project Report was recently produced by AIAA's Nuclear and Future Flight Propulsion Technical Committee and is currently in peer review. The Report provides recommended design practices for conceptual engineering studies of nuclear fusion space propulsion systems. Discussion and recommendations are made on key topics including design reference missions, degree of technological extrapolation and concomitant risk, thoroughness in calculating mass properties (nominal mass properties, weight-growth contingency and propellant margins, and specific impulse), and thoroughness in calculating power generation and usage (power-flow, power contingencies, specific power). The report represents a general consensus of the nuclear fusion space propulsion system conceptual design community and proposes 15 recommendations. This paper expands on the Report by providing specific examples illustrating how to apply each of the recommendations.

Improving treatment of neurodevelopmental disorders: recommendations based on preclinical studies.

PubMed

Homberg, Judith R; Kyzar, Evan J; Stewart, Adam Michael; Nguyen, Michael; Poudel, Manoj K; Echevarria, David J; Collier, Adam D; Gaikwad, Siddharth; Klimenko, Viktor M; Norton, William; Pittman, Julian; Nakamura, Shun; Koshiba, Mamiko; Yamanouchi, Hideo; Apryatin, Sergey A; Scattoni, Maria Luisa; Diamond, David M; Ullmann, Jeremy F P; Parker, Matthew O; Brown, Richard E; Song, Cai; Kalueff, Allan V

2016-01-01

Neurodevelopmental disorders (NDDs) are common and severely debilitating. Their chronic nature and reliance on both genetic and environmental factors makes studying NDDs and their treatment a challenging task. Herein, the authors discuss the neurobiological mechanisms of NDDs, and present recommendations on their translational research and therapy, outlined by the International Stress and Behavior Society. Various drugs currently prescribed to treat NDDs also represent a highly diverse group. Acting on various neurotransmitter and physiological systems, these drugs often lack specificity of action, and are commonly used to treat multiple other psychiatric conditions. There has also been relatively little progress in the development of novel medications to treat NDDs. Based on clinical, preclinical and translational models of NDDs, our recommendations cover a wide range of methodological approaches and conceptual strategies. To improve pharmacotherapy and drug discovery for NDDs, we need a stronger emphasis on targeting multiple endophenotypes, a better dissection of genetic/epigenetic factors or "hidden heritability," and a careful consideration of potential developmental/trophic roles of brain neurotransmitters. The validity of animal NDD models can be improved through discovery of novel (behavioral, physiological and neuroimaging) biomarkers, applying proper environmental enrichment, widening the spectrum of model organisms, targeting developmental trajectories of NDD-related behaviors and comorbid conditions beyond traditional NDDs. While these recommendations cannot be addressed all in once, our increased understanding of NDD pathobiology may trigger innovative cross-disciplinary research expanding beyond traditional methods and concepts.

Guidelines on eosinophilic esophagitis: evidence-based statements and recommendations for diagnosis and management in children and adults.

PubMed

Lucendo, Alfredo J; Molina-Infante, Javier; Arias, Ángel; von Arnim, Ulrike; Bredenoord, Albert J; Bussmann, Christian; Amil Dias, Jorge; Bove, Mogens; González-Cervera, Jesús; Larsson, Helen; Miehlke, Stephan; Papadopoulou, Alexandra; Rodríguez-Sánchez, Joaquín; Ravelli, Alberto; Ronkainen, Jukka; Santander, Cecilio; Schoepfer, Alain M; Storr, Martin A; Terreehorst, Ingrid; Straumann, Alex; Attwood, Stephen E

2017-04-01

Eosinophilic esophagitis (EoE) is one of the most prevalent esophageal diseases and the leading cause of dysphagia and food impaction in children and young adults. This underlines the importance of optimizing diagnosys and treatment of the condition, especially after the increasing amount of knowledge on EoE recently published. Therefore, the UEG, EAACI ESPGHAN, and EUREOS deemed it necessary to update the current guidelines regarding conceptual and epidemiological aspects, diagnosis, and treatment of EoE. General methodology according to the Appraisal of Guidelines for Research and Evaluation (AGREE) II and the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system was used in order to comply with current standards of evidence assessment in formulation of recommendations. An extensive literature search was conducted up to August 2015 and periodically updated. The working group consisted of gastroenterologists, allergists, pediatricians, otolaryngologists, pathologists, and epidemiologists. Systematic evidence-based reviews were performed based upon relevant clinical questions with respect to patient-important outcomes. The guidelines include updated concept of EoE, evaluated information on disease epidemiology, risk factors, associated conditions, and natural history of EoE in children and adults. Diagnostic conditions and criteria, the yield of diagnostic and disease monitoring procedures, and evidence-based statements and recommendation on the utility of the several treatment options for patients EoE are provided. Recommendations on how to choose and implement treatment and long-term management are provided based on expert opinion and best clinical practice. Evidence-based recommendations for EoE diagnosis, treatment modalities, and patients' follow up are proposed in the guideline.

Image Recommendation Algorithm Using Feature-Based Collaborative Filtering

NASA Astrophysics Data System (ADS)

Kim, Deok-Hwan

As the multimedia contents market continues its rapid expansion, the amount of image contents used in mobile phone services, digital libraries, and catalog service is increasing remarkably. In spite of this rapid growth, users experience high levels of frustration when searching for the desired image. Even though new images are profitable to the service providers, traditional collaborative filtering methods cannot recommend them. To solve this problem, in this paper, we propose feature-based collaborative filtering (FBCF) method to reflect the user's most recent preference by representing his purchase sequence in the visual feature space. The proposed approach represents the images that have been purchased in the past as the feature clusters in the multi-dimensional feature space and then selects neighbors by using an inter-cluster distance function between their feature clusters. Various experiments using real image data demonstrate that the proposed approach provides a higher quality recommendation and better performance than do typical collaborative filtering and content-based filtering techniques.

Intelligent system for topic survey in MEDLINE by keyword recommendation and learning text characteristics.

PubMed

Tanaka, M; Nakazono, S; Matsuno, H; Tsujimoto, H; Kitamura, Y; Miyano, S

2000-01-01

We have implemented a system for assisting experts in selecting MEDLINE records for database construction purposes. This system has two specific features: The first is a learning mechanism which extracts characteristics in the abstracts of MEDLINE records of interest as patterns. These patterns reflect selection decisions by experts and are used for screening the records. The second is a keyword recommendation system which assists and supplements experts' knowledge in unexpected cases. Combined with a conventional keyword-based information retrieval system, this system may provide an efficient and comfortable environment for MEDLINE record selection by experts. Some computational experiments are provided to prove that this idea is useful.

EULAR recommendations for conducting clinical studies and/or clinical trials in systemic vasculitis: focus on anti‐neutrophil cytoplasm antibody‐associated vasculitis

PubMed Central

Hellmich, Bernhard; Flossmann, Oliver; Gross, Wolfgang L; Bacon, Paul; Cohen‐Tervaert, Jan Willem; Guillevin, Loic; Jayne, David; Mahr, Alfred; Merkel, Peter A; Raspe, Heiner; Scott, David G I; Witter, James; Yazici, Hasan; Luqmani, Raashid A

2007-01-01

Objectives To develop the European League Against Rheumatism (EULAR) recommendations for conducting clinical studies and/or clinical trials in systemic vasculitis. Methods An expert consensus group was formed consisting of rheumatologists, nephrologists and specialists in internal medicine representing five European countries and the USA, a clinical epidemiologist and representatives from regulatory agencies. Using an evidence‐based and expert opinion‐based approach in accordance with the standardised EULAR operating procedures, the group identified nine topics for a systematic literature search through a modified Delphi technique. On the basis of research questions posed by the group, recommendations were derived for conducting clinical studies and/or clinical trials in systemic vasculitis. Results Based on the results of the literature research, the expert committee concluded that sufficient evidence to formulate guidelines on conducting clinical trials was available only for anti‐neutrophil cytoplasm antibody‐associated vasculitides (AAV). It was therefore decided to focus the recommendations on these diseases. Recommendations for conducting clinical trials in AAV were elaborated and are presented in this summary document. It was decided to consider vasculitis‐specific issues rather than general issues of trial methodology. The recommendations deal with the following areas related to clinical studies of vasculitis: definitions of disease, activity states, outcome measures, eligibility criteria, trial design including relevant end points, and biomarkers. A number of aspects of trial methodology were deemed important for future research. Conclusions On the basis of expert opinion, recommendations for conducting clinical trials in AAV were formulated. Furthermore, the expert committee identified a strong need for well‐designed research in non‐AAV systemic vasculitides. PMID:17170053

Increasing consumer demand for tobacco treatments: Ten design recommendations for clinicians and healthcare systems.

PubMed

Woods, Susan Swartz; Jaén, Carlos Roberto

2010-03-01

Health professionals play an important role in addressing patient tobacco use in clinical settings. While there is clear evidence that identifying tobacco use and assisting smokers in quitting affects outcomes, challenges to improve routine, clinician-delivered tobacco intervention persist. The Consumer Demand Initiative has identified simple design principles to increase consumers' use of proven tobacco treatments. Applying these design strategies to activities across the healthcare system, we articulate ten recommendations that can be implemented in the context of most clinical systems where most clinicians work. The recommendations are: (1) reframe the definition of success, (2) portray proven treatments as the best care, (3) redesign the 5A's of tobacco intervention, (4) be ready to deliver the right treatment at the right time, (5) move tobacco from the social history to the problem list, (6) use words as therapy and language that makes sense, (7) fit tobacco treatment into clinical team workflows, (8) embed tobacco treatment into health information technology, (9) make every encounter an opportunity to intervene, and (10) end social disparities for tobacco users. Clinical systems need to change to improve tobacco treatment implementation. The consumer- and clinician-centered recommendations provide a roadmap that focuses on increasing clinician performance through greater understanding of the clinician's role in helping tobacco users, highlighting the value of evidence-based tobacco treatments, employing shared decision-making skills, and integrating routine tobacco treatment into clinical system routines. Published by Elsevier Inc.

Changing Paradigms: From Schooling to Schools as Adaptive Recommendation Systems

ERIC Educational Resources Information Center

Petersen, Anne Kristine; Christiansen, Rene B.; Gynther, Karsten

2017-01-01

The paper explores a shift in education from educational systems requiring student adaptation to educational recommendation systems adapting to students' individual needs. The paper discusses the concept of adaptation as addressed in educational research and draws on the system theory of Heinz von Foerster to shed light on how the educational…

Growing Wikipedia Across Languages via Recommendation.

PubMed

Wulczyn, Ellery; West, Robert; Zia, Leila; Leskovec, Jure

2016-04-01

The different Wikipedia language editions vary dramatically in how comprehensive they are. As a result, most language editions contain only a small fraction of the sum of information that exists across all Wikipedias. In this paper, we present an approach to filling gaps in article coverage across different Wikipedia editions. Our main contribution is an end-to-end system for recommending articles for creation that exist in one language but are missing in another. The system involves identifying missing articles, ranking the missing articles according to their importance, and recommending important missing articles to editors based on their interests. We empirically validate our models in a controlled experiment involving 12,000 French Wikipedia editors. We find that personalizing recommendations increases editor engagement by a factor of two. Moreover, recommending articles increases their chance of being created by a factor of 3.2. Finally, articles created as a result of our recommendations are of comparable quality to organically created articles. Overall, our system leads to more engaged editors and faster growth of Wikipedia with no effect on its quality.

Growing Wikipedia Across Languages via Recommendation

PubMed Central

Wulczyn, Ellery; West, Robert; Zia, Leila; Leskovec, Jure

2016-01-01

The different Wikipedia language editions vary dramatically in how comprehensive they are. As a result, most language editions contain only a small fraction of the sum of information that exists across all Wikipedias. In this paper, we present an approach to filling gaps in article coverage across different Wikipedia editions. Our main contribution is an end-to-end system for recommending articles for creation that exist in one language but are missing in another. The system involves identifying missing articles, ranking the missing articles according to their importance, and recommending important missing articles to editors based on their interests. We empirically validate our models in a controlled experiment involving 12,000 French Wikipedia editors. We find that personalizing recommendations increases editor engagement by a factor of two. Moreover, recommending articles increases their chance of being created by a factor of 3.2. Finally, articles created as a result of our recommendations are of comparable quality to organically created articles. Overall, our system leads to more engaged editors and faster growth of Wikipedia with no effect on its quality. PMID:27819073

Systems Perspective of Amazon Mechanical Turk for Organizational Research: Review and Recommendations

PubMed Central

Keith, Melissa G.; Tay, Louis; Harms, Peter D.

2017-01-01

Amazon Mechanical Turk (MTurk) is becoming a prevalent source of quick and cost effective data for organizational research, but there are questions about the appropriateness of the platform for organizational research. To answer these questions, we conducted an integrative review based on 75 papers evaluating the MTurk platform and 250 MTurk samples used in organizational research. This integrative review provides four contributions: (1) we analyze the trends associated with the use of MTurk samples in organizational research; (2) we develop a systems perspective (recruitment system, selection system, and work management system) to synthesize and organize the key factors influencing data collected on MTurk that may affect generalizability and data quality; (3) within each factor, we also use available MTurk samples from the organizational literature to analyze key issues (e.g., sample characteristics, use of attention checks, payment); and (4) based on our review, we provide specific recommendations and a checklist for data reporting in order to improve data transparency and enable further research on this issue. PMID:28848474

Learning Materials Recommendation Using Good Learners' Ratings and Content-Based Filtering

ERIC Educational Resources Information Center

Ghauth, Khairil Imran; Abdullah, Nor Aniza

2010-01-01

The enormity of the amount of learning materials in e-learning has led to the difficulty of locating suitable learning materials for a particular learning topic, creating the need for recommendation tools within a learning context. In this paper, we aim to address this need by proposing a novel e-learning recommender system framework that is based…

Automated recommendation for cervical cancer screening and surveillance.

PubMed

Wagholikar, Kavishwar B; MacLaughlin, Kathy L; Casey, Petra M; Kastner, Thomas M; Henry, Michael R; Hankey, Ronald A; Peters, Steve G; Greenes, Robert A; Chute, Christopher G; Liu, Hongfang; Chaudhry, Rajeev

2014-01-01

Because of the complexity of cervical cancer prevention guidelines, clinicians often fail to follow best-practice recommendations. Moreover, existing clinical decision support (CDS) systems generally recommend a cervical cytology every three years for all female patients, which is inappropriate for patients with abnormal findings that require surveillance at shorter intervals. To address this problem, we developed a decision tree-based CDS system that integrates national guidelines to provide comprehensive guidance to clinicians. Validation was performed in several iterations by comparing recommendations generated by the system with those of clinicians for 333 patients. The CDS system extracted relevant patient information from the electronic health record and applied the guideline model with an overall accuracy of 87%. Providers without CDS assistance needed an average of 1 minute 39 seconds to decide on recommendations for management of abnormal findings. Overall, our work demonstrates the feasibility and potential utility of automated recommendation system for cervical cancer screening and surveillance.

World Health Organization recommendations are often strong based on low confidence in effect estimates.

PubMed

Alexander, Paul E; Bero, Lisa; Montori, Victor M; Brito, Juan Pablo; Stoltzfus, Rebecca; Djulbegovic, Benjamin; Neumann, Ignacio; Rave, Supriya; Guyatt, Gordon

2014-06-01

Expert guideline panelists are sometimes reluctant to offer weak/conditional/contingent recommendations. Grading of Recommendations Assessment, Development and Evaluation (GRADE) guidance warns against strong recommendations when confidence in effect estimates is low or very low, suggesting that such recommendations may seldom be justified. We aim to characterize the classification of strength of recommendations and confidence in estimates in World Health Organization (WHO) guidelines that used the GRADE approach and graded both strength and confidence (GRADEd). We reviewed all WHO guidelines (January 2007 to December 2012), identified those that were GRADEd, and, in these, examined the classifications of strong and weak and associated confidence in estimates (high, moderate, low, and very low). We identified 116 WHO guidelines in which 43 (37%) were GRADEd and had 456 recommendations, of which 289 (63.4%) were strong and 167 (36.6%) were conditional/weak. Of the 289 strong recommendations, 95 (33.0%) were based on evidence warranting low confidence in estimates and 65 (22.5%) on evidence warranting very low confidence in estimates (55.5% strong recommendations overall based on low or very low confidence in estimates). Strong recommendations based on low or very low confidence estimates are very frequently made in WHO guidelines. Further study to determine the reasons for such high uncertainty recommendations is warranted. Copyright © 2014 Elsevier Inc. All rights reserved.

A Machine Learning Recommender System to Tailor Preference Assessments to Enhance Person-Centered Care Among Nursing Home Residents.

PubMed

Gannod, Gerald C; Abbott, Katherine M; Van Haitsma, Kimberly; Martindale, Nathan; Heppner, Alexandra

2018-05-21

Nursing homes (NHs) using the Preferences for Everyday Living Inventory (PELI-NH) to assess important preferences and provide person-centered care find the number of items (72) to be a barrier to using the assessment. Using a sample of n = 255 NH resident responses to the PELI-NH, we used the 16 preference items from the MDS 3.0 Section F to develop a machine learning recommender system to identify additional PELI-NH items that may be important to specific residents. Much like the Netflix recommender system, our system is based on the concept of collaborative filtering whereby insights and predictions (e.g., filters) are created using the interests and preferences of many users. The algorithm identifies multiple sets of "you might also like" patterns called association rules, based upon responses to the 16 MDS preferences that recommends an additional set of preferences with a high likelihood of being important to a specific resident. In the evaluation of the combined apriori and logistic regression approach, we obtained a high recall performance (i.e., the ratio of correctly predicted preferences compared with all predicted preferences and nonpreferences) and high precision (i.e., the ratio of correctly predicted rules with respect to the rules predicted to be true) of 80.2% and 79.2%, respectively. The recommender system successfully provides guidance on how to best tailor the preference items asked of residents and can support preference capture in busy clinical environments, contributing to the feasibility of delivering person-centered care.

Orbit determination software development for microprocessor based systems: Evaluation and recommendations

NASA Technical Reports Server (NTRS)

Shenitz, C. M.; Mcgarry, F. E.; Tasaki, K. K.

1980-01-01

A guide is presented for National Aeronautics and Space Administration management personnel who stand to benefit from the lessons learned in developing microprocessor-based flight dynamics software systems. The essential functional characteristics of microprocessors are presented. The relevant areas of system support software are examined, as are the distinguishing characteristics of flight dynamics software. Design examples are provided to illustrate the major points presented, and actual development experience obtained in this area is provided as evidence to support the conclusions reached.

Guidelines on eosinophilic esophagitis: evidence-based statements and recommendations for diagnosis and management in children and adults

PubMed Central

Molina-Infante, Javier; Arias, Ángel; von Arnim, Ulrike; Bredenoord, Albert J; Bussmann, Christian; Amil Dias, Jorge; Bove, Mogens; González-Cervera, Jesús; Larsson, Helen; Miehlke, Stephan; Papadopoulou, Alexandra; Rodríguez-Sánchez, Joaquín; Ravelli, Alberto; Ronkainen, Jukka; Santander, Cecilio; Schoepfer, Alain M; Storr, Martin A; Terreehorst, Ingrid; Straumann, Alex; Attwood, Stephen E

2017-01-01

Introduction Eosinophilic esophagitis (EoE) is one of the most prevalent esophageal diseases and the leading cause of dysphagia and food impaction in children and young adults. This underlines the importance of optimizing diagnosys and treatment of the condition, especially after the increasing amount of knowledge on EoE recently published. Therefore, the UEG, EAACI ESPGHAN, and EUREOS deemed it necessary to update the current guidelines regarding conceptual and epidemiological aspects, diagnosis, and treatment of EoE. Methods General methodology according to the Appraisal of Guidelines for Research and Evaluation (AGREE) II and the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system was used in order to comply with current standards of evidence assessment in formulation of recommendations. An extensive literature search was conducted up to August 2015 and periodically updated. The working group consisted of gastroenterologists, allergists, pediatricians, otolaryngologists, pathologists, and epidemiologists. Systematic evidence-based reviews were performed based upon relevant clinical questions with respect to patient-important outcomes. Results The guidelines include updated concept of EoE, evaluated information on disease epidemiology, risk factors, associated conditions, and natural history of EoE in children and adults. Diagnostic conditions and criteria, the yield of diagnostic and disease monitoring procedures, and evidence-based statements and recommendation on the utility of the several treatment options for patients EoE are provided. Recommendations on how to choose and implement treatment and long-term management are provided based on expert opinion and best clinical practice. Conclusion Evidence-based recommendations for EoE diagnosis, treatment modalities, and patients’ follow up are proposed in the guideline. PMID:28507746

Recommended conceptual optical system design for China's Large Optical-infrared Telescope (LOT).

PubMed

Ma, Donglin

2018-01-08

Recently, China is planning to construct a new large optical-infrared telescope (LOT), in which the aperture of the primary mirror is as large as 12m. China's LOT is a general-purpose telescope, which is aimed to work with multiple scientific instruments such as spectrographs. Based on the requirements of LOT telescope, we have compared the performance of Ritchey-Chrétien (RC) design and Aplanatic-Gregorian (AG) design from the perspective of scientific performance and construction cost. By taking the primary focal ratio, Nasmyth focal ratio, and telescope's site condition into consideration, we finally recommend a RC f/1.6 design configuration for LOT's Nasmyth telescope system. Unlike the general identical configuration, we choose a non-identical configuration for the telescope system which has a shorter Cassegrain focal ratio compared to the designed Nasmyth focal ratio. The non-identical design can allow for a shorter back focal distance and therefore a shorter telescope fork to guarantee the gravitational stability of the whole telescope structure, as well as relatively lower construction cost. Detailed analysis for the feasibility of our recommended design is provided in this paper.

Evaluation of the utility of 99m Tc-MDP bone scintigraphy versus MIBG scintigraphy and cross-sectional imaging for staging patients with neuroblastoma.

PubMed

Gauguet, Jean-Marc; Pace-Emerson, Tamara; Grant, Frederick D; Shusterman, Suzanne; DuBois, Steven G; Frazier, A Lindsay; Voss, Stephan D

2017-11-01

Accurate staging of neuroblastoma requires multiple imaging examinations. The purpose of this study was to determine the relative contribution of 99m Tc-methylene diphosphonate (MDP) bone scintigraphy (bone scan) versus metaiodobenzylguanidine scintigraphy (MIBG scan) for accurate staging of neuroblastoma. A medical record search by the identified patients with neuroblastoma from 1993 to 2012 who underwent both MIBG and bone scan for disease staging. Cross-sectional imaging was used to corroborate the scintigraphy results. Clinical records were used to correlate imaging findings with clinical staging and patient management. One hundred thirty-two patients underwent both MIBG and bone scan for diagnosis. All stage 1 (n = 12), 2 (n = 8), and 4S (n = 4) patients had a normal bone scan with no skeletal MIBG uptake. Six of 30 stage 3 patients had false (+) bone scans. In the 78 stage 4 patients, 58/78 (74%) were both skeletal MIBG(+)/bone scan (+). In 56 of the 58 cases, skeletal involvement detected with MIBG was equal to or greater than that detected by bone scan. Only 3/78 had (-) skeletal MIBG uptake and (+) bone scans; all 3 had other sites of metastatic disease. Five of 78 had (+) skeletal MIBG with a (-) bone scan, while 12/78 had no skeletal involvement by either MIBG or bone scan. In no case did a positive bone scan alone determine a stage 4 designation. In the staging of neuroblastoma, 99m Tc-MDP bone scintigraphy does not identify unique sites of disease that affect disease stage or clinical management, and in the majority of cases bone scans can be omitted from the routine neuroblastoma staging algorithm. © 2017 Wiley Periodicals, Inc.

Designing and Developing a Novel Hybrid Adaptive Learning Path Recommendation System (ALPRS) for Gamification Mathematics Geometry Course

ERIC Educational Resources Information Center

Su, Chung-Ho

2017-01-01

Since recommendation systems possess the advantage of adaptive recommendation, they have gradually been applied to e-learning systems to recommend subsequent learning content for learners. However, problems exist in current learning recommender systems available to students in that they are often general learning content and unable to offer…

Core components for effective infection prevention and control programmes: new WHO evidence-based recommendations.

PubMed

Storr, Julie; Twyman, Anthony; Zingg, Walter; Damani, Nizam; Kilpatrick, Claire; Reilly, Jacqui; Price, Lesley; Egger, Matthias; Grayson, M Lindsay; Kelley, Edward; Allegranzi, Benedetta

2017-01-01

Health care-associated infections (HAI) are a major public health problem with a significant impact on morbidity, mortality and quality of life. They represent also an important economic burden to health systems worldwide. However, a large proportion of HAI are preventable through effective infection prevention and control (IPC) measures. Improvements in IPC at the national and facility level are critical for the successful containment of antimicrobial resistance and the prevention of HAI, including outbreaks of highly transmissible diseases through high quality care within the context of universal health coverage. Given the limited availability of IPC evidence-based guidance and standards, the World Health Organization (WHO) decided to prioritize the development of global recommendations on the core components of effective IPC programmes both at the national and acute health care facility level, based on systematic literature reviews and expert consensus. The aim of the guideline development process was to identify the evidence and evaluate its quality, consider patient values and preferences, resource implications, and the feasibility and acceptability of the recommendations. As a result, 11 recommendations and three good practice statements are presented here, including a summary of the supporting evidence, and form the substance of a new WHO IPC guideline.

Nearly total absence of pulmonary perfusion with corresponding technetium-99m MDP and gallium-67 uptake in a patient with mediastinal neuroblastoma

DOE Office of Scientific and Technical Information (OSTI.GOV)

Garty, I.; Koren, A.; Moguilner, G.

1985-08-01

A case of unilateral nearly total hypoperfusion of the left lung in a 13-month-old girl is presented. The combination of the lung hypoperfusion and accumulation of the Tc-99m MDP and Ga-67 citrate in the same area suggested the preoperative diagnosis of mediastinal neuroblastoma. Explorative thoracotomy revealed the presence of a neuroblastoma compressing the left lung pedicle. The described scintigraphic appearance in the pediatric age group is suggested as typical of mediastinal neuroblastoma. This pathology should be included in the following gamuts in nuclear medicine: unilateral decrease or absent lung perfusion, unilateral diffuse chest uptake of Ga-67 citrate, and unilateral pulmonarymore » uptake in bone scintigraphy.« less

[Clinical practice guidelines for systemic lupus erythematosus: Recommendations for general clinical management].

PubMed

Trujillo-Martín, María M; Rúa-Figueroa Fernández de Larrinoa, Iñigo; Ruíz-Irastorza, Guillermo; Pego-Reigosa, José María; Sabio Sánchez, José Mario; Serrano-Aguilar, Pedro

2016-05-06

Systemic lupus erythematosus (SLE) is a complex rheumatic multisystemic disease of autoimmune origin with significant potential morbidity and mortality. It is one of the most common autoimmune diseases with an estimated prevalence of 20-150 cases per 100,000 inhabitants. The clinical spectrum of SLE is wide and variable both in clinical manifestations and severity. This prompted the Spanish Ministry of Health, Social Services and Equality to promote and fund the development of a clinical practice guideline (CPG) for the clinical care of SLE patients within the Programme of CPG in the National Health System which coordinates GuiaSalud. This CPG is is intended as the reference tool in the Spanish National Health System in order to support the comprehensive clinical management of people with SLE by all health professionals involved, regardless of specialty and level of care, helping to standardize and improve the quality of clinical decisions in our context in order to improve the health outcomes of the people affected. The purpose of this document is to present and discuss the rationale of the recommendations on the general management of SLE, specifically, clinical follow-up, general therapeutic approach, healthy lifestyles, photoprotection, and training programmes for patients. These recommendations are based on the best available scientific evidence, on discussion and the consensus of expert groups. Copyright © 2016 Elsevier España, S.L.U. All rights reserved.

Improved collaborative filtering recommendation algorithm of similarity measure

NASA Astrophysics Data System (ADS)

Zhang, Baofu; Yuan, Baoping

2017-05-01

The Collaborative filtering recommendation algorithm is one of the most widely used recommendation algorithm in personalized recommender systems. The key is to find the nearest neighbor set of the active user by using similarity measure. However, the methods of traditional similarity measure mainly focus on the similarity of user common rating items, but ignore the relationship between the user common rating items and all items the user rates. And because rating matrix is very sparse, traditional collaborative filtering recommendation algorithm is not high efficiency. In order to obtain better accuracy, based on the consideration of common preference between users, the difference of rating scale and score of common items, this paper presents an improved similarity measure method, and based on this method, a collaborative filtering recommendation algorithm based on similarity improvement is proposed. Experimental results show that the algorithm can effectively improve the quality of recommendation, thus alleviate the impact of data sparseness.

Do pharmacy staff recommend evidenced-based smoking cessation products? A pseudo patron study.

PubMed

Chiang, P P C; Chapman, S

2006-06-01

To determine whether pharmacy staff recommend evidence-based smoking cessation aids. Pseudo patron visit to 50 randomly selected Sydney pharmacies where the pseudo patron enquired about the 'best' way to quit smoking and about the efficacy of a non-evidence-based cessation product, NicoBloc. Nicotine replacement therapy was universally stocked and the first product recommended by 90% of pharmacies. After prompting, 60% of pharmacies, either also recommended NicoBloc or deferred to 'customer choice'. About 34% disparaged the product. Evidence-based smoking cessation advice in Sydney pharmacies is fragile and may be compromised by commercial concerns. Smokers should be provided with independent point-of-sale summaries of evidence of cessation product effectiveness and warned about unsubstantiated claims.

Using Principles of Evidence-Based Practice to Improve Prescriptive Recommendations

ERIC Educational Resources Information Center

Schraw, Gregory; Patall, Erika A.

2013-01-01

We draw on the evidence-based practice (EBP) literature to consider the relationship between empirical results reported in primary research journals and prescriptive recommendations for practice based on those results. We argue that the relationship between individual empirical findings and practice should be mediated by two additional steps in…

Evidence-based Review, Grade of Recommendation, and Suggested Treatment Recommendations for Melasma

PubMed Central

Sarma, Nilendu; Chakraborty, Sayantani; Poojary, Shital A.; Rathi, Sanjay; Kumaran, Sendhil; Nirmal, Balakrishnan; Felicita, Joan; Sarkar, Rashmi; Jaiswal, Prashansa; D’Souza, Paschal; Donthula, Nagaraju; Sethi, Sumit; Ailawadi, Pallavi; Joseph, Bebisha

2017-01-01

Treatment of melasma is known to be less satisfactory, often incomplete, and relapse is frequent. Although many treatment options are available, they are either known to be unsafe on long-term use or their long-term safety profile is unknown. Patients often use various drugs, even topical steroid-based preparation without any medical supervision for long period of time, making the skin unsuitable for many of the drugs available. Thus, there has been gross disparity among the treating physician about what drugs and what regimen are best suitable for various categories of melasma patients and in different situations. With this background, numerous newer drugs, mostly combinations of some proprietary molecules or even unknown plant extracts, have flooded the market for the management of melasma. Information on efficacy or safety of these products are almost unknown. Studies on Asian people, especially Indian population, are far less commonly available. Therapeutic guideline for use on Indian patients with melasma is almost missing. Extrapolation of data from Caucasian people for use on Asian people may not be scientifically justifiable because Caucasian and Asian people are known to have inherent difference in their response as well as tolerance to the drugs used for melasma. With this background, we have extensively evaluated, following a strict, scientifically designed protocol, all the available studies on melasma management till May 2016 and prepared this document on level of evidence, grade of recommendation and suggested therapeutic guideline for melasma as per the method proposed by Oxford Centre of Evidence-Based Medicine. Various ethical, social, logical, regional, and economic issues in the context of Indian and similar populations were given due importance while preparing the suggested therapeutic recommendation. PMID:29204385

An Effective News Recommendation Method for Microblog User

PubMed Central

Gu, Wanrong; Dong, Shoubin; Zeng, Zhizhao; He, Jinchao

2014-01-01

Recommending news stories to users, based on their preferences, has long been a favourite domain for recommender systems research. Traditional systems strive to satisfy their user by tracing users' reading history and choosing the proper candidate news articles to recommend. However, most of news websites hardly require any user to register before reading news. Besides, the latent relations between news and microblog, the popularity of particular news, and the news organization are not addressed or solved efficiently in previous approaches. In order to solve these issues, we propose an effective personalized news recommendation method based on microblog user profile building and sub class popularity prediction, in which we propose a news organization method using hybrid classification and clustering, implement a sub class popularity prediction method, and construct user profile according to our actual situation. We had designed several experiments compared to the state-of-the-art approaches on a real world dataset, and the experimental results demonstrate that our system significantly improves the accuracy and diversity in mass text data. PMID:24983011

Occupational therapy-based and evidence-supported recommendations for assessment and exercises in hand osteoarthritis.

PubMed

Kjeken, Ingvild

2011-12-01

The aims of this study were to develop recommendations for occupational therapy assessment and design of hand exercise programmes in patients with hand osteoarthritis. An expert group followed a Delphi procedure to reach consensus for up to 10 recommendations for assessment and exercises, respectively. Thereafter, an evidence-based approach was used to identify and appraise research evidence supporting each recommendation, before the recommendations were validated by the expert group. The process resulted in 10 recommendations for assessment and eight for design of exercise programmes. The literature search revealed that there is a paucity of clinical trials to guide recommendations for hand osteoarthritis, and the evidence for the majority of the recommendations was based on expert opinions. Also, even if a systematic review demonstrates some evidence for the efficacy of strength training exercises in hand OA, the evidence for any specific exercise is limited to expert opinions. A first set of recommendations for assessment and exercise in hand osteoarthritis has been developed. For many of the recommendations there is a paucity of research evidence. High-quality studies are therefore needed to establish a high level of evidence concerning functional assessment and the effect of hand exercises in hand osteoarthritis.

Identification of potent orally active factor Xa inhibitors based on conjugation strategy and application of predictable fragment recommender system.

PubMed

Ishihara, Tsukasa; Koga, Yuji; Iwatsuki, Yoshiyuki; Hirayama, Fukushi

2015-01-15

Anticoagulant agents have emerged as a promising class of therapeutic drugs for the treatment and prevention of arterial and venous thrombosis. We investigated a series of novel orally active factor Xa inhibitors designed using our previously reported conjugation strategy to boost oral anticoagulant effect. Structural optimization of anthranilamide derivative 3 as a lead compound with installation of phenolic hydroxyl group and extensive exploration of the P1 binding element led to the identification of 5-chloro-N-(5-chloro-2-pyridyl)-3-hydroxy-2-{[4-(4-methyl-1,4-diazepan-1-yl)benzoyl]amino}benzamide (33, AS1468240) as a potent factor Xa inhibitor with significant oral anticoagulant activity. We also reported a newly developed Free-Wilson-like fragment recommender system based on the integration of R-group decomposition with collaborative filtering for the structural optimization process. Copyright © 2014 Elsevier Ltd. All rights reserved.

Research-based recommendations for implementing international service-learning.

PubMed

Amerson, Roxanne

2014-01-01

An increasing number of schools of nursing are incorporating international service-learning and/or immersion experiences into their curriculum to promote cultural competence. The purpose of this paper is to identify research-based recommendations for implementing an international service-learning program. A review of literature was conducted in the Cumulative Index of Nursing and Allied Health Literature database using the keywords international, immersion, cultural competence, nursing, and international service-learning. Additional references were located from the reference lists of related articles. Planning of international or immersion experiences requires consideration of the type of country, the length of time, and design of the program; the use of a service-learning framework; opportunities that require the student to live and work in the community, provide hands-on care, participate in unstructured activities, and make home visits; and a method of reflection. Increasing cultural competence does not require foreign travel, but it does necessitate that students are challenged to move outside their comfort zone and work directly with diverse populations. These research-based recommendations may be used either internationally or locally to promote the most effective service-learning opportunities for nursing students. © 2014.

wayGoo recommender system: personalized recommendations for events scheduling, based on static and real-time information

NASA Astrophysics Data System (ADS)

Thanos, Konstantinos-Georgios; Thomopoulos, Stelios C. A.

2016-05-01

wayGoo is a fully functional application whose main functionalities include content geolocation, event scheduling, and indoor navigation. However, significant information about events do not reach users' attention, either because of the size of this information or because some information comes from real - time data sources. The purpose of this work is to facilitate event management operations by prioritizing the presented events, based on users' interests using both, static and real - time data. Through the wayGoo interface, users select conceptual topics that are interesting for them. These topics constitute a browsing behavior vector which is used for learning users' interests implicitly, without being intrusive. Then, the system estimates user preferences and return an events list sorted from the most preferred one to the least. User preferences are modeled via a Naïve Bayesian Network which consists of: a) the `decision' random variable corresponding to users' decision on attending an event, b) the `distance' random variable, modeled by a linear regression that estimates the probability that the distance between a user and each event destination is not discouraging, ` the seat availability' random variable, modeled by a linear regression, which estimates the probability that the seat availability is encouraging d) and the `relevance' random variable, modeled by a clustering - based collaborative filtering, which determines the relevance of each event users' interests. Finally, experimental results show that the proposed system contribute essentially to assisting users in browsing and selecting events to attend.

John Dewey's Report of 1924 and his Recommendations on the Turkish Educational System Revisited.

ERIC Educational Resources Information Center

Turan, Selahattin

2000-01-01

Explains that in 1924, John Dewey went to Turkey in order to observe and analyze the educational system and offer restructuring recommendations. Aims to reevaluate the significance of Dewey's visit to Turkey, his recommendations, and his report on the Turkish educational system. Analyses his 30 page report. (CMK)

Paediatric Intestinal Pseudo-obstruction: Evidence and Consensus-based Recommendations From an ESPGHAN-Led Expert Group.

PubMed

Thapar, Nikhil; Saliakellis, Efstratios; Benninga, Marc A; Borrelli, Osvaldo; Curry, Joe; Faure, Christophe; De Giorgio, Roberto; Gupte, Girish; Knowles, Charles H; Staiano, Annamaria; Vandenplas, Yvan; Di Lorenzo, Carlo

2018-06-01

Chronic intestinal pseudo-obstructive (CIPO) conditions are considered the most severe disorders of gut motility. They continue to present significant challenges in clinical care despite considerable recent progress in our understanding of pathophysiology, resulting in unacceptable levels of morbidity and mortality. Major contributors to the disappointing lack of progress in paediatric CIPO include a dearth of clarity and uniformity across all aspects of clinical care from definition and diagnosis to management. In order to assist medical care providers in identifying, evaluating, and managing children with CIPO, experts in this condition within the European Society for Pediatric Gastroenterology, Hepatology, and Nutrition as well as selected external experts, were charged with the task of developing a uniform document of evidence- and consensus-based recommendations. Ten clinically relevant questions addressing terminology, diagnostic, therapeutic, and prognostic topics were formulated. A systematic literature search was performed from inception to June 2017 using a number of established electronic databases as well as repositories. The approach of the Grading of Recommendations Assessment, Development and Evaluation (GRADE) was applied to evaluate outcome measures for the research questions. Levels of evidence and quality of evidence were assessed using the classification system of the Oxford Centre for Evidence-Based Medicine (diagnosis) and the GRADE system (treatment). Each of the recommendations were discussed, finalized, and voted upon using the nominal voting technique to obtain consensus. This evidence- and consensus-based position paper provides recommendations specifically for chronic intestinal pseudo-obstruction in infants and children. It proposes these be termed paediatric intestinal pseudo-obstructive (PIPO) disorders to distinguish them from adult onset CIPO. The manuscript provides guidance on the diagnosis, evaluation, and treatment of children

Comprehensive Fault Tolerance and Science-Optimal Attitude Planning for Spacecraft Applications

NASA Astrophysics Data System (ADS)

Nasir, Ali

Spacecraft operate in a harsh environment, are costly to launch, and experience unavoidable communication delay and bandwidth constraints. These factors motivate the need for effective onboard mission and fault management. This dissertation presents an integrated framework to optimize science goal achievement while identifying and managing encountered faults. Goal-related tasks are defined by pointing the spacecraft instrumentation toward distant targets of scientific interest. The relative value of science data collection is traded with risk of failures to determine an optimal policy for mission execution. Our major innovation in fault detection and reconfiguration is to incorporate fault information obtained from two types of spacecraft models: one based on the dynamics of the spacecraft and the second based on the internal composition of the spacecraft. For fault reconfiguration, we consider possible changes in both dynamics-based control law configuration and the composition-based switching configuration. We formulate our problem as a stochastic sequential decision problem or Markov Decision Process (MDP). To avoid the computational complexity involved in a fully-integrated MDP, we decompose our problem into multiple MDPs. These MDPs include planning MDPs for different fault scenarios, a fault detection MDP based on a logic-based model of spacecraft component and system functionality, an MDP for resolving conflicts between fault information from the logic-based model and the dynamics-based spacecraft models" and the reconfiguration MDP that generates a policy optimized over the relative importance of the mission objectives versus spacecraft safety. Approximate Dynamic Programming (ADP) methods for the decomposition of the planning and fault detection MDPs are applied. To show the performance of the MDP-based frameworks and ADP methods, a suite of spacecraft attitude planning case studies are described. These case studies are used to analyze the content and

Reducing Delay in Diagnosis: Multistage Recommendation Tracking.

PubMed

Wandtke, Ben; Gallagher, Sarah

2017-11-01

The purpose of this study was to determine whether a multistage tracking system could improve communication between health care providers, reducing the risk of delay in diagnosis related to inconsistent communication and tracking of radiology follow-up recommendations. Unconditional recommendations for imaging follow-up of all diagnostic imaging modalities excluding mammography (n = 589) were entered into a database and tracked through a multistage tracking system for 13 months. Tracking interventions were performed for patients for whom completion of recommended follow-up imaging could not be identified 1 month after the recommendation due date. Postintervention compliance with the follow-up recommendation required examination completion or clinical closure (i.e., biopsy, limited life expectancy or death, or subspecialist referral). Baseline radiology information system checks performed 1 month after the recommendation due date revealed timely completion of 43.1% of recommended imaging studies at our institution before intervention. Three separate tracking interventions were studied, showing effectiveness between 29.0% and 57.8%. The multistage tracking system increased the examination completion rate to 70.5% (a 52% increase) and reduced the rate of unknown follow-up compliance and the associated risk of delay in diagnosis to 13.9% (a 74% decrease). Examinations completed after tracking intervention generated revenue of 4.1 times greater than the labor cost. Performing sequential radiology recommendation tracking interventions can substantially reduce the rate of unknown follow-up compliance and add value to the health system. Unknown follow-up compliance is a risk factor for delay in diagnosis, a form of preventable medical error commonly identified in malpractice claims involving radiologists and office-based practitioners.

Alleviating bias leads to accurate and personalized recommendation

NASA Astrophysics Data System (ADS)

Qiu, Tian; Wang, Tian-Tian; Zhang, Zi-Ke; Zhong, Li-Xin; Chen, Guang

2013-11-01

Recommendation bias towards objects has been found to have an impact on personalized recommendation, since objects present heterogeneous characteristics in some network-based recommender systems. In this article, based on a biased heat conduction recommendation algorithm (BHC) which considers the heterogeneity of the target objects, we propose a heterogeneous heat conduction algorithm (HHC), by further taking the heterogeneity of the source objects into account. Tested on three real datasets, the Netflix, RYM and MovieLens, the HHC algorithm is found to present better recommendation in both the accuracy and diversity than two benchmark algorithms, i.e., the original BHC and a hybrid algorithm of heat conduction and mass diffusion (HHM), while not requiring any other accessorial information or parameter. Moreover, the HHC algorithm also elevates the recommendation accuracy on cold objects, referring to the so-called cold-start problem. Eigenvalue analyses show that, the HHC algorithm effectively alleviates the recommendation bias towards objects with different level of popularity, which is beneficial to solving the accuracy-diversity dilemma.

MicroRNA based Pan-Cancer Diagnosis and Treatment Recommendation.

PubMed

Cheerla, Nikhil; Gevaert, Olivier

2017-01-13

The current state-of-the-art in cancer diagnosis and treatment is not ideal; diagnostic tests are accurate but invasive, and treatments are "one-size fits-all" instead of being personalized. Recently, miRNA's have garnered significant attention as cancer biomarkers, owing to their ease of access (circulating miRNA in the blood) and stability. There have been many studies showing the effectiveness of miRNA data in diagnosing specific cancer types, but few studies explore the role of miRNA in predicting treatment outcome. Here we go a step further, using tissue miRNA and clinical data across 21 cancers from the 'The Cancer Genome Atlas' (TCGA) database. We use machine learning techniques to create an accurate pan-cancer diagnosis system, and a prediction model for treatment outcomes. Finally, using these models, we create a web-based tool that diagnoses cancer and recommends the best treatment options. We achieved 97.2% accuracy for classification using a support vector machine classifier with radial basis. The accuracies improved to 99.9-100% when climbing up the embryonic tree and classifying cancers at different stages. We define the accuracy as the ratio of the total number of instances correctly classified to the total instances. The classifier also performed well, achieving greater than 80% sensitivity for many cancer types on independent validation datasets. Many miRNAs selected by our feature selection algorithm had strong previous associations to various cancers and tumor progression. Then, using miRNA, clinical and treatment data and encoding it in a machine-learning readable format, we built a prognosis predictor model to predict the outcome of treatment with 85% accuracy. We used this model to create a tool that recommends personalized treatment regimens. Both the diagnosis and prognosis model, incorporating semi-supervised learning techniques to improve their accuracies with repeated use, were uploaded online for easy access. Our research is a step

Development and empirical user-centered evaluation of semantically-based query recommendation for an electronic health record search engine.

PubMed

Hanauer, David A; Wu, Danny T Y; Yang, Lei; Mei, Qiaozhu; Murkowski-Steffy, Katherine B; Vydiswaran, V G Vinod; Zheng, Kai

2017-03-01

The utility of biomedical information retrieval environments can be severely limited when users lack expertise in constructing effective search queries. To address this issue, we developed a computer-based query recommendation algorithm that suggests semantically interchangeable terms based on an initial user-entered query. In this study, we assessed the value of this approach, which has broad applicability in biomedical information retrieval, by demonstrating its application as part of a search engine that facilitates retrieval of information from electronic health records (EHRs). The query recommendation algorithm utilizes MetaMap to identify medical concepts from search queries and indexed EHR documents. Synonym variants from UMLS are used to expand the concepts along with a synonym set curated from historical EHR search logs. The empirical study involved 33 clinicians and staff who evaluated the system through a set of simulated EHR search tasks. User acceptance was assessed using the widely used technology acceptance model. The search engine's performance was rated consistently higher with the query recommendation feature turned on vs. off. The relevance of computer-recommended search terms was also rated high, and in most cases the participants had not thought of these terms on their own. The questions on perceived usefulness and perceived ease of use received overwhelmingly positive responses. A vast majority of the participants wanted the query recommendation feature to be available to assist in their day-to-day EHR search tasks. Challenges persist for users to construct effective search queries when retrieving information from biomedical documents including those from EHRs. This study demonstrates that semantically-based query recommendation is a viable solution to addressing this challenge. Published by Elsevier Inc.

Layout Study and Application of Mobile App Recommendation Approach Based On Spark Streaming Framework

NASA Astrophysics Data System (ADS)

Wang, H. T.; Chen, T. T.; Yan, C.; Pan, H.

2018-05-01

For App recommended areas of mobile phone software, made while using conduct App application recommended combined weighted Slope One algorithm collaborative filtering algorithm items based on further improvement of the traditional collaborative filtering algorithm in cold start, data matrix sparseness and other issues, will recommend Spark stasis parallel algorithm platform, the introduction of real-time streaming streaming real-time computing framework to improve real-time software applications recommended.

Learning Management System with Prediction Model and Course-Content Recommendation Module

ERIC Educational Resources Information Center

Evale, Digna S.

2017-01-01

Aim/Purpose: This study is an attempt to enhance the existing learning management systems today through the integration of technology, particularly with educational data mining and recommendation systems. Background: It utilized five-year historical data to find patterns for predicting student performance in Java Programming to generate…

A new collaborative recommendation approach based on users clustering using artificial bee colony algorithm.

PubMed

Ju, Chunhua; Xu, Chonghuan

2013-01-01

Although there are many good collaborative recommendation methods, it is still a challenge to increase the accuracy and diversity of these methods to fulfill users' preferences. In this paper, we propose a novel collaborative filtering recommendation approach based on K-means clustering algorithm. In the process of clustering, we use artificial bee colony (ABC) algorithm to overcome the local optimal problem caused by K-means. After that we adopt the modified cosine similarity to compute the similarity between users in the same clusters. Finally, we generate recommendation results for the corresponding target users. Detailed numerical analysis on a benchmark dataset MovieLens and a real-world dataset indicates that our new collaborative filtering approach based on users clustering algorithm outperforms many other recommendation methods.

A New Collaborative Recommendation Approach Based on Users Clustering Using Artificial Bee Colony Algorithm

PubMed Central

Ju, Chunhua

2013-01-01

Although there are many good collaborative recommendation methods, it is still a challenge to increase the accuracy and diversity of these methods to fulfill users' preferences. In this paper, we propose a novel collaborative filtering recommendation approach based on K-means clustering algorithm. In the process of clustering, we use artificial bee colony (ABC) algorithm to overcome the local optimal problem caused by K-means. After that we adopt the modified cosine similarity to compute the similarity between users in the same clusters. Finally, we generate recommendation results for the corresponding target users. Detailed numerical analysis on a benchmark dataset MovieLens and a real-world dataset indicates that our new collaborative filtering approach based on users clustering algorithm outperforms many other recommendation methods. PMID:24381525

Context-Aware Recommender Systems for Learning: A Survey and Future Challenges

ERIC Educational Resources Information Center

Verbert, K.; Manouselis, N.; Ochoa, X.; Wolpers, M.; Drachsler, H.; Bosnic, I.; Duval, E.

2012-01-01

Recommender systems have been researched extensively by the Technology Enhanced Learning (TEL) community during the last decade. By identifying suitable resources from a potentially overwhelming variety of choices, such systems offer a promising approach to facilitate both learning and teaching tasks. As learning is taking place in extremely…

Human factors in the Naval Air Systems Command: Computer based training

DOE Office of Scientific and Technical Information (OSTI.GOV)

Seamster, T.L.; Snyder, C.E.; Terranova, M.

1988-01-01

Military standards applied to the private sector contracts have a substantial effect on the quality of Computer Based Training (CBT) systems procured for the Naval Air Systems Command. This study evaluated standards regulating the following areas in CBT development and procurement: interactive training systems, cognitive task analysis, and CBT hardware. The objective was to develop some high-level recommendations for evolving standards that will govern the next generation of CBT systems. One of the key recommendations is that there be an integration of the instructional systems development, the human factors engineering, and the software development standards. Recommendations were also made formore » task analysis and CBT hardware standards. (9 refs., 3 figs.)« less

IA-Regional-Radio - Social Network for Radio Recommendation

NASA Astrophysics Data System (ADS)

Dziczkowski, Grzegorz; Bougueroua, Lamine; Wegrzyn-Wolska, Katarzyna

This chapter describes the functions of a system proposed for the music hit recommendation from social network data base. This system carries out the automatic collection, evaluation and rating of music reviewers and the possibility for listeners to rate musical hits and recommendations deduced from auditor's profiles in the form of regional Internet radio. First, the system searches and retrieves probable music reviews from the Internet. Subsequently, the system carries out an evaluation and rating of those reviews. From this list of music hits, the system directly allows notation from our application. Finally, the system automatically creates the record list diffused each day depending on the region, the year season, the day hours and the age of listeners. Our system uses linguistics and statistic methods for classifying music opinions and data mining techniques for recommendation part needed for recorded list creation. The principal task is the creation of popular intelligent radio adaptive on auditor's age and region - IA-Regional-Radio.

Performance evaluation of recommendation algorithms on Internet of Things services

NASA Astrophysics Data System (ADS)

Mashal, Ibrahim; Alsaryrah, Osama; Chung, Tein-Yaw

2016-06-01

Internet of Things (IoT) is the next wave of industry revolution that will initiate many services, such as personal health care and green energy monitoring, which people may subscribe for their convenience. Recommending IoT services to the users based on objects they own will become very crucial for the success of IoT. In this work, we introduce the concept of service recommender systems in IoT by a formal model. As a first attempt in this direction, we have proposed a hyper-graph model for IoT recommender system in which each hyper-edge connects users, objects, and services. Next, we studied the usefulness of traditional recommendation schemes and their hybrid approaches on IoT service recommendation (IoTSRS) based on existing well known metrics. The preliminary results show that existing approaches perform reasonably well but further extension is required for IoTSRS. Several challenges were discussed to point out the direction of future development in IoTSR.

High-Value Consults: A Curriculum to Promote Point-of-Care, Evidence-Based Recommendations.

PubMed

Nandiwada, Deepa Rani; Kohli, Amar; McNamara, Megan; Smith, Kenneth J; Zimmer, Shanta; McNeil, Melissa; Spagnoletti, Carla; Rubio, Doris; Berlacher, Kathryn

2017-10-01

In an era when value-based care is paramount, teaching trainees to explicitly communicate the evidence behind recommendations fosters high-value care (HVC) in the consultation process. To implement an HVC consult curriculum highlighting the need for clear consult questions, evidence-based recommendations to improve consult teaching, clinical decision-making, and the educational value of consults. A pilot curriculum was implemented for residents on cardiology consult electives utilizing faculty and fellows as evidence-based medicine (EBM) coaches. The curriculum included an online module, an EBM teaching point template, EBM presentations on rounds, and "coach" feedback on notes. A total of 15 residents and 4 fellows on cardiology consults participated, and 87% (13 of 15) of residents on consults felt the curriculum was educationally valuable. A total of 80% (72 of 90) of residents on general medicine rotations responded to the survey, and 25 of 72 residents (35%) had a consult with the EBM template. General medicine teams felt the EBM teaching points affected clinical decision-making (48%, 12 of 25) and favored dissemination of the curriculum (90%, 72 of 80). Checklist-guided chart review showed a 22% improvement in evidence-based summaries behind recommendations (7 of 36 precurriculum to 70 of 146 charts postcurriculum, P  = .015). The HVC consult curriculum during a cardiology elective was perceived by residents to influence clinical decision-making and evidence-based recommendations, and was found to be educationally valuable on both parties in the consult process.

A reconsideration of negative ratings for network-based recommendation

NASA Astrophysics Data System (ADS)

Hu, Liang; Ren, Liang; Lin, Wenbin

2018-01-01

Recommendation algorithms based on bipartite networks have become increasingly popular, thanks to their accuracy and flexibility. Currently, many of these methods ignore users' negative ratings. In this work, we propose a method to exploit negative ratings for the network-based inference algorithm. We find that negative ratings play a positive role regardless of sparsity of data sets. Furthermore, we improve the efficiency of our method and compare it with the state-of-the-art algorithms. Experimental results show that the present method outperforms the existing algorithms.

World Health Organization strong recommendations based on low-quality evidence (study quality) are frequent and often inconsistent with GRADE guidance.

PubMed

Alexander, Paul E; Brito, Juan P; Neumann, Ignacio; Gionfriddo, Michael R; Bero, Lisa; Djulbegovic, Benjamin; Stoltzfus, Rebecca; Montori, Victor M; Norris, Susan L; Schünemann, Holger J; Guyatt, Gordon H

2016-04-01

In 2007 the World Health Organization (WHO) adopted the GRADE system for development of public health guidelines. Previously we found that many strong recommendations issued by WHO are based on evidence for which there is only low or very low confidence in the estimates of effect (discordant recommendations). GRADE guidance indicates that such discordant recommendations are rarely appropriate but suggests five paradigmatic situations in which discordant recommendations may be warranted. We sought to provide insight into the many discordant recommendations in WHO guidelines. We examined all guidelines that used the GRADE method and were approved by the WHO Guideline Review Committee between 2007 and 2012. Teams of reviewers independently abstracted data from eligible guidelines and classified recommendations either into one of the five paradigms for appropriately-formulated discordant recommendations or into three additional categories in which discordant recommendations were inconsistent with GRADE guidance: 1) the evidence warranted moderate or high confidence (a misclassification of evidence) rather than low or very low confidence; 2) good practice statements; or 3) uncertainty in the estimates of effect would best lead to a conditional (weak) recommendation. The 33 eligible guidelines included 160 discordant recommendations, of which 98 (61.3%) addressed drug interventions and 132 (82.5%) provided some rationale (though not entirely explicit at times) for the strong recommendation. Of 160 discordant recommendations, 25 (15.6%) were judged consistent with one of the five paradigms for appropriate recommendations; 33 (21%) were based on evidence warranting moderate or high confidence in the estimates of effect; 29 (18%) were good practice statements; and 73 (46%) warranted a conditional, rather than a strong recommendation. WHO discordant recommendations are often inconsistent with GRADE guidance, possibly threatening the integrity of the process. Further training

Data base design for a worldwide multicrop information system

NASA Technical Reports Server (NTRS)

Driggers, W. G.; Downs, J. M.; Hickman, J. R.; Packard, R. L. (Principal Investigator)

1979-01-01

A description of the USDA Application Test System data base design approach and resources is presented. The data is described in detail by category, with emphasis on those characteristics which influenced the design most. It was concluded that the use of a generalized data base in support of crop assessment is a sound concept. The IDMS11 minicomputer base system is recommended for this purpose.

Digital case-based learning system in school.

PubMed

Gu, Peipei; Guo, Jiayang

2017-01-01

With the continuing growth of multi-media learning resources, it is important to offer methods helping learners to explore and acquire relevant learning information effectively. As services that organize multi-media learning materials together to support programming learning, the digital case-based learning system is needed. In order to create a case-oriented e-learning system, this paper concentrates on the digital case study of multi-media resources and learning processes with an integrated framework. An integration of multi-media resources, testing and learning strategies recommendation as the learning unit is proposed in the digital case-based learning framework. The learning mechanism of learning guidance, multi-media materials learning and testing feedback is supported in our project. An improved personalized genetic algorithm which incorporates preference information and usage degree into the crossover and mutation process is proposed to assemble the personalized test sheet for each learner. A learning strategies recommendation solution is proposed to recommend learning strategies for learners to help them to learn. The experiments are conducted to prove that the proposed approaches are capable of constructing personalized sheets and the effectiveness of the framework.

Digital case-based learning system in school

PubMed Central

Gu, Peipei

2017-01-01

With the continuing growth of multi-media learning resources, it is important to offer methods helping learners to explore and acquire relevant learning information effectively. As services that organize multi-media learning materials together to support programming learning, the digital case-based learning system is needed. In order to create a case-oriented e-learning system, this paper concentrates on the digital case study of multi-media resources and learning processes with an integrated framework. An integration of multi-media resources, testing and learning strategies recommendation as the learning unit is proposed in the digital case-based learning framework. The learning mechanism of learning guidance, multi-media materials learning and testing feedback is supported in our project. An improved personalized genetic algorithm which incorporates preference information and usage degree into the crossover and mutation process is proposed to assemble the personalized test sheet for each learner. A learning strategies recommendation solution is proposed to recommend learning strategies for learners to help them to learn. The experiments are conducted to prove that the proposed approaches are capable of constructing personalized sheets and the effectiveness of the framework. PMID:29107965

Aesthetic Surgery of the Buttocks Using Implants: Practice-Based Recommendations.

PubMed

Senderoff, Douglas M

2016-05-01

The demand for gluteal enhancement has increased rapidly in the past few years. In this Continuing Medical Education (CME) article, the evaluation, surgical planning, operative technique, and management of potential complications of gluteal augmentation using solid silicone implants are discussed. Practice-based recommendations are presented along with a review of the scientific literature. The intramuscular and subfascial technique is described along with a discussion of the advantages and disadvantages of each approach. Guidelines for implant selection, placement, and revisional procedures are presented along with recommendations for maximizing successful outcomes. © 2016 The American Society for Aesthetic Plastic Surgery, Inc. Reprints and permission: journals.permissions@oup.com.

The Physician Recommendation Coding System (PhyReCS): A Reliable and Valid Method to Quantify the Strength of Physician Recommendations During Clinical Encounters

PubMed Central

Scherr, Karen A.; Fagerlin, Angela; Williamson, Lillie D.; Davis, J. Kelly; Fridman, Ilona; Atyeo, Natalie; Ubel, Peter A.

2016-01-01

Background Physicians’ recommendations affect patients’ treatment choices. However, most research relies on physicians’ or patients’ retrospective reports of recommendations, which offer a limited perspective and have limitations such as recall bias. Objective To develop a reliable and valid method to measure the strength of physician recommendations using direct observation of clinical encounters. Methods Clinical encounters (n = 257) were recorded as part of a larger study of prostate cancer decision making. We used an iterative process to create the 5-point Physician Recommendation Coding System (PhyReCS). To determine reliability, research assistants double-coded 50 transcripts. To establish content validity, we used one-way ANOVAs to determine whether relative treatment recommendation scores differed as a function of which treatment patients received. To establish concurrent validity, we examined whether patients’ perceived treatment recommendations matched our coded recommendations. Results The PhyReCS was highly reliable (Krippendorf’s alpha =. 89, 95% CI [.86, .91]). The average relative treatment recommendation score for each treatment was higher for individuals who received that particular treatment. For example, the average relative surgery recommendation score was higher for individuals who received surgery versus radiation (mean difference = .98, SE = .18, p < .001) or active surveillance (mean difference = 1.10, SE = .14, p < .001). Patients’ perceived recommendations matched coded recommendations 81% of the time. Conclusion The PhyReCS is a reliable and valid way to capture the strength of physician recommendations. We believe that the PhyReCS would be helpful for other researchers who wish to study physician recommendations, an important part of patient decision making. PMID:27343015

A vertex similarity index for better personalized recommendation

NASA Astrophysics Data System (ADS)

Chen, Ling-Jiao; Zhang, Zi-Ke; Liu, Jin-Hu; Gao, Jian; Zhou, Tao

2017-01-01

Recommender systems benefit us in tackling the problem of information overload by predicting our potential choices among diverse niche objects. So far, a variety of personalized recommendation algorithms have been proposed and most of them are based on similarities, such as collaborative filtering and mass diffusion. Here, we propose a novel vertex similarity index named CosRA, which combines advantages of both the cosine index and the resource-allocation (RA) index. By applying the CosRA index to real recommender systems including MovieLens, Netflix and RYM, we show that the CosRA-based method has better performance in accuracy, diversity and novelty than some benchmark methods. Moreover, the CosRA index is free of parameters, which is a significant advantage in real applications. Further experiments show that the introduction of two turnable parameters cannot remarkably improve the overall performance of the CosRA index.

18F-FDG PET/CT in breast cancer: Evidence-based recommendations in initial staging.

PubMed

Caresia Aroztegui, Ana Paula; García Vicente, Ana María; Alvarez Ruiz, Soledad; Delgado Bolton, Roberto Carlos; Orcajo Rincon, Javier; Garcia Garzon, Jose Ramon; de Arcocha Torres, Maria; Garcia-Velloso, Maria Jose

2017-10-01

Current guidelines do not systematically recommend 18F-FDG PET/CT for breast cancer staging; and the recommendations and level of evidence supporting its use in different groups of patients vary among guidelines. This review summarizes the evidence about the role of 18F-FDG PET/CT in breast cancer staging and the therapeutic and prognostic impact accumulated in the last decade. Other related aspects, such as the association of metabolic information with biology and prognosis are considered and evidence-based recommendations for the use of 18F-FDG PET/CT in breast cancer staging are offered. We systematically searched MEDLINE for articles reporting studies with at least 30 patients related to clinical questions following the Problem/Population, Intervention, Comparison, and Outcome framework. We critically reviewed the selected articles and elaborated evidence tables structuring the summarized information into methodology, results, and limitations. The level of evidence and the grades of recommendation for the use of 18F-FDG PET/CT in different contexts are summarized. Level III evidence supports the use of 18F-FDG PET/CT for initial staging in patients with recently diagnosed breast cancer; the diagnostic and therapeutic impact of the 18F-FDG PET/CT findings is sufficient for a weak recommendation in this population. In patients with locally advanced breast cancer, level II evidence supports the use of 18F-FDG PET/CT for initial staging; the diagnostic and therapeutic impact of the 18F-FDG PET/CT findings is sufficient for a strong recommendation in this population. In patients with recently diagnosed breast cancer, the metabolic information from baseline 18F-FDG PET/CT is associated with tumor biology and has prognostic implications, supported by level II evidence. In conclusion, 18F-FDG PET/CT is not recommended for staging all patients with early breast cancer, although evidence of improved regional and systemic staging supports its use in locally advanced

Improving the recommender algorithms with the detected communities in bipartite networks

NASA Astrophysics Data System (ADS)

Zhang, Peng; Wang, Duo; Xiao, Jinghua

2017-04-01

Recommender system offers a powerful tool to make information overload problem well solved and thus gains wide concerns of scholars and engineers. A key challenge is how to make recommendations more accurate and personalized. We notice that community structures widely exist in many real networks, which could significantly affect the recommendation results. By incorporating the information of detected communities in the recommendation algorithms, an improved recommendation approach for the networks with communities is proposed. The approach is examined in both artificial and real networks, the results show that the improvement on accuracy and diversity can be 20% and 7%, respectively. This reveals that it is beneficial to classify the nodes based on the inherent properties in recommender systems.

Evidence-Based Consensus Recommendations for Colposcopy Practice for Cervical Cancer Prevention in the United States.

PubMed

Wentzensen, Nicolas; Massad, L Stewart; Mayeaux, Edward J; Khan, Michelle J; Waxman, Alan G; Einstein, Mark H; Conageski, Christine; Schiffman, Mark H; Gold, Michael A; Apgar, Barbara S; Chelmow, David; Choma, Kim K; Darragh, Teresa M; Gage, Julia C; Garcia, Francisco A R; Guido, Richard S; Jeronimo, Jose A; Liu, Angela; Mathews, Cara A; Mitchell, Martha M; Moscicki, Anna-Barbara; Novetsky, Akiva P; Papasozomenos, Theognosia; Perkins, Rebecca B; Silver, Michelle I; Smith, Katie M; Stier, Elizabeth A; Tedeschi, Candice A; Werner, Claudia L; Huh, Warner K

2017-10-01

The American Society for Colposcopy and Cervical Pathology (ASCCP) Colposcopy Standards recommendations address the role of colposcopy and directed biopsy for cervical cancer prevention in the United States (US). The recommendations were developed by an expert working group appointed by ASCCP's Board of Directors. An extensive literature review was conducted and supplemented by a systematic review and meta-analysis of unpublished data. In addition, a survey of practicing colposcopists was conducted to assess current colposcopy practice in the US. Recommendations were approved by the working group members, and the final revisions were made based on comments received from the public. The recommendations cover terminology, risk-based colposcopy, colposcopy procedures, and colposcopy adjuncts. The ASCCP Colposcopy Standards recommendations are an important step toward raising the standard of colposcopy services delivered to women in the US. Because cervical cancer screening programs are currently undergoing important changes that may affect colposcopy performance, updates to some of the current recommendations may be necessary in the future.

Optimal Navigation of Self-Propelled Colloids in Microstructured Mazes

NASA Astrophysics Data System (ADS)

Yang, Yuguang; Bevan, Michael

Controlling navigation of self-propelled microscopic `robots' subject to random Brownian motion in complex microstructured environments (e.g., porous media, tumor vasculature) is important to many emerging applications (e.g., enhanced oil recovery, drug delivery). In this work, we design an optimal feedback policy to navigate an active self-propelled colloidal rod in complex mazes with various obstacle types. Actuation of the rods is modelled based on a light-controlled osmotic flow mechanism, which produces different propulsion velocities along the rod's long axis. Actuator-parameterized Langevin equations, with soft rod-obstacle repulsive interactions, are developed to describe the system dynamics. A Markov decision process (MDP) framework is used for optimal policy calculations with design goals of colloidal rods reaching target end points in minimum time. Simulations show that optimal MDP-based policies are able to control rod trajectories to reach target regions order-of-magnitudes faster than uncontrolled rods, which diverges as maze complexity increases. An efficient multi-graph based implementation for MDP is also presented, which scales linearly with the maze dimension.

Recommender engine for continuous-time quantum Monte Carlo methods

NASA Astrophysics Data System (ADS)

Huang, Li; Yang, Yi-feng; Wang, Lei

2017-03-01

Recommender systems play an essential role in the modern business world. They recommend favorable items such as books, movies, and search queries to users based on their past preferences. Applying similar ideas and techniques to Monte Carlo simulations of physical systems boosts their efficiency without sacrificing accuracy. Exploiting the quantum to classical mapping inherent in the continuous-time quantum Monte Carlo methods, we construct a classical molecular gas model to reproduce the quantum distributions. We then utilize powerful molecular simulation techniques to propose efficient quantum Monte Carlo updates. The recommender engine approach provides a general way to speed up the quantum impurity solvers.

Systems for grading the quality of evidence and the strength of recommendations I: Critical appraisal of existing approaches The GRADE Working Group

PubMed Central

Atkins, David; Eccles, Martin; Flottorp, Signe; Guyatt, Gordon H; Henry, David; Hill, Suzanne; Liberati, Alessandro; O'Connell, Dianne; Oxman, Andrew D; Phillips, Bob; Schünemann, Holger; Edejer, Tessa Tan-Torres; Vist, Gunn E; Williams, John W

2004-01-01

Background A number of approaches have been used to grade levels of evidence and the strength of recommendations. The use of many different approaches detracts from one of the main reasons for having explicit approaches: to concisely characterise and communicate this information so that it can easily be understood and thereby help people make well-informed decisions. Our objective was to critically appraise six prominent systems for grading levels of evidence and the strength of recommendations as a basis for agreeing on characteristics of a common, sensible approach to grading levels of evidence and the strength of recommendations. Methods Six prominent systems for grading levels of evidence and strength of recommendations were selected and someone familiar with each system prepared a description of each of these. Twelve assessors independently evaluated each system based on twelve criteria to assess the sensibility of the different approaches. Systems used by 51 organisations were compared with these six approaches. Results There was poor agreement about the sensibility of the six systems. Only one of the systems was suitable for all four types of questions we considered (effectiveness, harm, diagnosis and prognosis). None of the systems was considered usable for all of the target groups we considered (professionals, patients and policy makers). The raters found low reproducibility of judgements made using all six systems. Systems used by 51 organisations that sponsor clinical practice guidelines included a number of minor variations of the six systems that we critically appraised. Conclusions All of the currently used approaches to grading levels of evidence and the strength of recommendations have important shortcomings. PMID:15615589

Shear Bond Strength of Al2O3 Sandblasted Y-TZP Ceramic to the Orthodontic Metal Bracket

PubMed Central

Byeon, Seon Mi; Lee, Min Ho; Bae, Tae Sung

2017-01-01

As the proportion of adult orthodontic treatment increases, mainly for aesthetic reasons, orthodontic brackets are directly attached to yttria-stabilized tetragonal zirconia polycrystal (Y-TZP) restorations. This, study analyzed the shear bond strength (SBS) between various surface treated Y-TZP and orthodontic metal brackets. The Y-TZP specimens were conditioned by 110 μm Al2O3 sandblasting, or sandblasting followed by coating with one of the primers (silane, MDP, or an MDP-containing silane primer). After surface treatment, the orthodontic metal bracket was bonded to the specimen using a resin cement, and then 24 h storage in water and thermal cycling (5000 cycles, 5–55 °C), SBS was measured. Surface roughness was analyzed for surface morphology, and X-ray photoelectron spectroscopy (XPS) was employed for characterization of the chemical bond between the Y-TZP and the MDP-based primers (MDP, MDP containing silane primer). It was found that after surface treatment, the surface roughness of all groups increased. The groups treated with 110 μm Al2O3 sandblasting and MDP, or MDP-containing silane primer showed the highest SBS values, at 11.92 ± 1.51 MPa and 13.36 ± 2.31 MPa, respectively. The SBS values significantly decreased in all the groups after thermal cycling. Results from XPS analysis demonstrated the presence of chemical bonds between Y-TZP and MDP. Thus, the application of MDP-based primers after Al2O3 sandblasting enhances the resin bond strength between Y-TZP and the orthodontic metal bracket. However, bonding durability of all the surface-treated groups decreased after thermal cycling. PMID:28772508

[Recommendations in neonatal resuscitation].

PubMed

2004-01-01

The recommendations for neonatal resuscitation are not always based on sufficient scientific evidence and thus expert consensus based on current research, knowledge, and experience are useful for formulating practical protocols that are easy to follow. The latest recommendations, in 2000, modified previously published recommendations and are included in the present text.

The Physician Recommendation Coding System (PhyReCS): A Reliable and Valid Method to Quantify the Strength of Physician Recommendations During Clinical Encounters.

PubMed

Scherr, Karen A; Fagerlin, Angela; Williamson, Lillie D; Davis, J Kelly; Fridman, Ilona; Atyeo, Natalie; Ubel, Peter A

2017-01-01

Physicians' recommendations affect patients' treatment choices. However, most research relies on physicians' or patients' retrospective reports of recommendations, which offer a limited perspective and have limitations such as recall bias. To develop a reliable and valid method to measure the strength of physician recommendations using direct observation of clinical encounters. Clinical encounters (n = 257) were recorded as part of a larger study of prostate cancer decision making. We used an iterative process to create the 5-point Physician Recommendation Coding System (PhyReCS). To determine reliability, research assistants double-coded 50 transcripts. To establish content validity, we used 1-way analyses of variance to determine whether relative treatment recommendation scores differed as a function of which treatment patients received. To establish concurrent validity, we examined whether patients' perceived treatment recommendations matched our coded recommendations. The PhyReCS was highly reliable (Krippendorf's alpha = 0.89, 95% CI [0.86, 0.91]). The average relative treatment recommendation score for each treatment was higher for individuals who received that particular treatment. For example, the average relative surgery recommendation score was higher for individuals who received surgery versus radiation (mean difference = 0.98, SE = 0.18, P < 0.001) or active surveillance (mean difference = 1.10, SE = 0.14, P < 0.001). Patients' perceived recommendations matched coded recommendations 81% of the time. The PhyReCS is a reliable and valid way to capture the strength of physician recommendations. We believe that the PhyReCS would be helpful for other researchers who wish to study physician recommendations, an important part of patient decision making. © The Author(s) 2016.

TDA Assessment of Recommendations for Space Data System Standards

NASA Technical Reports Server (NTRS)

Posner, E. C.; Stevens, R.

1984-01-01

NASA is participating in the development of international standards for space data systems. Recommendations for standards thus far developed are assessed. The proposed standards for telemetry coding and packet telemetry provide worthwhile benefit to the DSN; their cost impact to the DSN should be small. Because of their advantage to the NASA space exploration program, their adoption should be supported by TDA, JPL, and OSTDS.

A framework for diversifying recommendation lists by user interest expansion.

PubMed

Zhang, Zhu; Zheng, Xiaolong; Zeng, Daniel Dajun

2016-08-01

Recommender systems have been widely used to discover users' preferences and recommend interesting items to users during this age of information load. Researchers in the field of recommender systems have realized that the quality of a top-N recommendation list involves not only relevance but also diversity. Most traditional recommendation algorithms are difficult to generate a diverse item list that can cover most of his/her interests for each user, since they mainly focus on predicting accurate items similar to the dominant interests of users. Additionally, they seldom exploit semantic information such as item tags and users' interest labels to improve recommendation diversity. In this paper, we propose a novel recommendation framework which mainly adopts an expansion strategy of user interests based on social tagging information. The framework enhances the diversity of users' preferences by expanding the sizes and categories of the original user-item interaction records, and then adopts traditional recommendation models to generate recommendation lists. Empirical evaluations on three real-world data sets show that our method can effectively improve the accuracy and diversity of item recommendation.

A framework for diversifying recommendation lists by user interest expansion

PubMed Central

Zhang, Zhu; Zeng, Daniel Dajun

2017-01-01

Recommender systems have been widely used to discover users’ preferences and recommend interesting items to users during this age of information load. Researchers in the field of recommender systems have realized that the quality of a top-N recommendation list involves not only relevance but also diversity. Most traditional recommendation algorithms are difficult to generate a diverse item list that can cover most of his/her interests for each user, since they mainly focus on predicting accurate items similar to the dominant interests of users. Additionally, they seldom exploit semantic information such as item tags and users’ interest labels to improve recommendation diversity. In this paper, we propose a novel recommendation framework which mainly adopts an expansion strategy of user interests based on social tagging information. The framework enhances the diversity of users’ preferences by expanding the sizes and categories of the original user-item interaction records, and then adopts traditional recommendation models to generate recommendation lists. Empirical evaluations on three real-world data sets show that our method can effectively improve the accuracy and diversity of item recommendation. PMID:28959089

Applications of CCSDS recommendations to Integrated Ground Data Systems (IGDS)

NASA Technical Reports Server (NTRS)

Mizuta, Hiroshi; Martin, Daniel; Kato, Hatsuhiko; Ihara, Hirokazu

1993-01-01

This paper describes an application of the CCSDS Principle Network (CPH) service model to communications network elements of a postulated Integrated Ground Data System (IGDS). Functions are drawn principally from COSMICS (Cosmic Information and Control System), an integrated space control infrastructure, and the Earth Observing System Data and Information System (EOSDIS) Core System (ECS). From functional requirements, this paper derives a set of five communications network partitions which, taken together, support proposed space control infrastructures and data distribution systems. Our functional analysis indicates that the five network partitions derived in this paper should effectively interconnect the users, centers, processors, and other architectural elements of an IGDS. This paper illustrates a useful application of the CCSDS (Consultive Committee for Space Data Systems) Recommendations to ground data system development.

Recommendation Systems for Geoscience Data Portals Built by Analyzing Usage Patterns

NASA Astrophysics Data System (ADS)

Crosby, C.; Nandigam, V.; Baru, C.

2009-04-01

Since its launch five years ago, the National Science Foundation-funded GEON Project (www.geongrid.org) has been providing access to a variety of geoscience data sets such as geologic maps and other geographic information system (GIS)-oriented data, paleontologic databases, gravity and magnetics data and LiDAR topography via its online portal interface. In addition to data, the GEON Portal also provides web-based tools and other resources that enable users to process and interact with data. Examples of these tools include functions to dynamically map and integrate GIS data, compute synthetic seismograms, and to produce custom digital elevation models (DEMs) with user defined parameters such as resolution. The GEON portal built on the Gridsphere-portal framework allows us to capture user interaction with the system. In addition to the site access statistics captured by tools like Google Analystics which capture hits per unit time, search key words, operating systems, browsers, and referring sites, we also record additional statistics such as which data sets are being downloaded and in what formats, processing parameters, and navigation pathways through the portal. With over four years of data now available from the GEON Portal, this record of usage is a rich resource for exploring how earth scientists discover and utilize online data sets. Furthermore, we propose that this data could ultimately be harnessed to optimize the way users interact with the data portal, design intelligent processing and data management systems, and to make recommendations on algorithm settings and other available relevant data. The paradigm of integrating popular and commonly used patterns to make recommendations to a user is well established in the world of e-commerce where users receive suggestions on books, music and other products that they may find interesting based on their website browsing and purchasing history, as well as the patterns of fellow users who have made similar

Integration of Evidence into a Detailed Clinical Model-based Electronic Nursing Record System

PubMed Central

Park, Hyeoun-Ae; Jeon, Eunjoo; Chung, Eunja

2012-01-01

Objectives The purpose of this study was to test the feasibility of an electronic nursing record system for perinatal care that is based on detailed clinical models and clinical practice guidelines in perinatal care. Methods This study was carried out in five phases: 1) generating nursing statements using detailed clinical models; 2) identifying the relevant evidence; 3) linking nursing statements with the evidence; 4) developing a prototype electronic nursing record system based on detailed clinical models and clinical practice guidelines; and 5) evaluating the prototype system. Results We first generated 799 nursing statements describing nursing assessments, diagnoses, interventions, and outcomes using entities, attributes, and value sets of detailed clinical models for perinatal care which we developed in a previous study. We then extracted 506 recommendations from nine clinical practice guidelines and created sets of nursing statements to be used for nursing documentation by grouping nursing statements according to these recommendations. Finally, we developed and evaluated a prototype electronic nursing record system that can provide nurses with recommendations for nursing practice and sets of nursing statements based on the recommendations for guiding nursing documentation. Conclusions The prototype system was found to be sufficiently complete, relevant, useful, and applicable in terms of content, and easy to use and useful in terms of system user interface. This study has revealed the feasibility of developing such an ENR system. PMID:22844649

Integrated Adult Education Data System. Policy Option Paper on Strategic Recommendation 8. First Edition.

ERIC Educational Resources Information Center

Porter, Dennis

This document addresses the recommendation contained in the 1989 California Strategic Plan for Adult Education for an integrated adult education data system. The recommendation proposes collecting and organizing community adult education information into groups of data on: program services, program delivery, learner characteristics, and learning…

Evidence-based recommendation on toothpaste use.

PubMed

Cury, Jaime Aparecido; Tenuta, Livia Maria Andalo

2014-01-01

Toothpaste can be used as a vehicle for substances to improve the oral health of individuals and populations. Therefore, it should be recommended based on the best scientific evidence available, and not on the opinion of authorities or specialists. Fluoride is the most important therapeutic substance used in toothpastes, adding to the effect of mechanical toothbrushing on dental caries control. The use of fluoride toothpaste to reduce caries in children and adults is strongly based on evidence, and is dependent on the concentration (minimum of 1000 ppm F) and frequency of fluoride toothpaste use (2'/day or higher). The risk of dental fluorosis due to toothpaste ingestion by children has been overestimated, since there is no evidence that: 1) fluoride toothpaste use should be postponed until the age of 3-4 or older, 2) low-fluoride toothpaste avoids fluorosis and 3) fluorosis has a detrimental effect on the quality of life of individuals exposed to fluoridated water and toothpaste. Among other therapeutic substances used in toothpastes, there is evidence that triclosan/copolymer reduce dental biofilm, gingivitis, periodontitis, calculus and halitosis, and that toothpastes containing stannous fluoride reduce biofilm and gingivitis.

E-Learning Personalization Based on Hybrid Recommendation Strategy and Learning Style Identification

ERIC Educational Resources Information Center

Klasnja-Milicevic, Aleksandra; Vesin, Boban; Ivanovic, Mirjana; Budimac, Zoran

2011-01-01

Personalized learning occurs when e-learning systems make deliberate efforts to design educational experiences that fit the needs, goals, talents, and interests of their learners. Researchers had recently begun to investigate various techniques to help teachers improve e-learning systems. In this paper, we describe a recommendation module of a…

YouTube Video Educational Package Increased Acceptance of Antibiotic Clinical Decision Support System Recommendations

PubMed Central

Heng, Shi Thong; Tan, Michelle; Young, Barnaby; Lye, David; Ng, Tat Ming

2017-01-01

Abstract Background Antibiotic clinical decision support systems (CDSS) were implemented to provide stewardship at the point of ordering of broad-spectrum antibiotics (piperacillin-tazobactam and carbapenems). We postulated that a YouTube based educational video package (EP) with quizzes can help to improve CDSS acceptance. Methods A before-after study was conducted in general wards at Tan Tock Seng Hospital from April 2016 to March 2017. Baseline data were collected for 6 months before EP was implemented and during the next 6 months with EP dissemination to all doctors. Acceptance of CDSS recommendations between both phases were compared. Independent factors associated with acceptance of specific CDSS recommendations were identified by logistic regression. Results Patients recruited before and after EP was 1642 and 1313 respectively. Overall CDSS acceptance rate was similar before and after EP. There was improved acceptance for recommendations for dose optimizaton, antibiotic optimization and set duration (Figures 1 and 2). Independent factors of CDSS acceptance for dose optimizaton, antibiotic optimization and set duration are shown in Table 1. EP implementation was independently associated with acceptance of recommendations to set duration and optimize antibiotics. Conclusion EP was independently associated with increased CDSS acceptance on antibiotic duration and antibiotic optimization. Although acceptance of dose optimization was improved, EP was not associated independently with acceptance of the recommendations. Figure 2 Acceptance of CDSS recommendations by classifications of recommendations Table 1 3 multivariate models of acceptance of CDSS recommendations on antibiotic optimization, dose optimization and duration setting Set duration Antibiotic optimization Dose optimization Factor Odds ratio [95% CI] Lung infection 2.71[2.13–3.45] 2.08[1.71–2.52] 2.79[2.19-3.55] Unknown sepsis source 1.73[1.27–2.35] – 1.44[1.05-1.96] Piperacillin

Analyzing recommender systems for health promotion using a multidisciplinary taxonomy: A scoping review.

PubMed

Hors-Fraile, Santiago; Rivera-Romero, Octavio; Schneider, Francine; Fernandez-Luque, Luis; Luna-Perejon, Francisco; Civit-Balcells, Anton; de Vries, Hein

2018-06-01

Recommender systems are information retrieval systems that provide users with relevant items (e.g., through messages). Despite their extensive use in the e-commerce and leisure domains, their application in healthcare is still in its infancy. These systems may be used to create tailored health interventions, thus reducing the cost of healthcare and fostering a healthier lifestyle in the population. This paper identifies, categorizes, and analyzes the existing knowledge in terms of the literature published over the past 10 years on the use of health recommender systems for patient interventions. The aim of this study is to understand the scientific evidence generated about health recommender systems, to identify any gaps in this field to achieve the United Nations Sustainable Development Goal 3 (SDG3) (namely, "Ensure healthy lives and promote well-being for all at all ages"), and to suggest possible reasons for these gaps as well as to propose some solutions. We conducted a scoping review, which consisted of a keyword search of the literature related to health recommender systems for patients in the following databases: ScienceDirect, PsycInfo, Association for Computing Machinery, IEEExplore, and Pubmed. Further, we limited our search to consider only English-language journal articles published in the last 10 years. The reviewing process comprised three researchers who filtered the results simultaneously. The quantitative synthesis was conducted in parallel by two researchers, who classified each paper in terms of four aspects-the domain, the methodological and procedural aspects, the health promotion theoretical factors and behavior change theories, and the technical aspects-using a new multidisciplinary taxonomy. Nineteen papers met the inclusion criteria and were included in the data analysis, for which thirty-three features were assessed. The nine features associated with the health promotion theoretical factors and behavior change theories were not observed in

Factors Associated With Inadequate Effectiveness of a Multidisciplinary Disease Management Program in Heart Failure Patients Stratified by Galectin 3 Level.

PubMed

Liu, Min-Hui; Wang, Chao-Hung; Chiou, Ai-Fu; Yang, Ning-I; Kuo, Li-Tang

2016-07-21

This study investigated whether multidisciplinary disease management programs (MDPs) exert the same effects in heart failure (HF) patients across risk levels stratified by galectin-3 (Gal-3) level and what factors are associated with inadequate effectiveness of MDP. We used a longitudinal follow-up design based on a previous randomized trial. A total of 355 stabilized hospitalized HF patients were enrolled. The effects of MDP on death and HF-related rehospitalization were analyzed according to Gal-3 levels. During the 4-year follow-up, Gal-3 levels predicted mortality and composite events (p < .001). Multivariable analysis demonstrated the event-lowering effect of MDP (hazard ratio [HR] = 0.49, p = .001 for death and HR = 0.50, p < .001 for composite events). However, the effect of MDP was inadequate for those with high Gal-3 levels (≥17.9 ng/ml), whose 4-year composite event rate was 43% in the MDP arm. Further analysis showed that, in patients with Gal-3 ≥ 17.9 ng/ml, the independent factors associated with a high composite event rate were no MDP, older age, worse New York Heart Association functional class, no angiotensin-converting enzyme inhibitor or angiotensin II receptor blocker use, higher predischarge natriuretic peptide levels, and wider QRS complexes. The effectiveness of MDP for HF patients at high risk was inadequate. Our findings identified the characteristics of these MDP nonresponders. Better integration of advanced care plans based on strategies guided by Gal-3 level is needed to improve care quality. © The Author(s) 2016.

Methodology for AACT evidence-based recommendations on the use of intravenous lipid emulsion therapy in poisoning.

PubMed

Gosselin, Sophie; Morris, Martin; Miller-Nesbitt, Andrea; Hoffman, Robert S; Hayes, Bryan D; Turgeon, Alexis F; Gilfix, Brian M; Grunbaum, Ami M; Bania, Theodore C; Thomas, Simon H L; Morais, José A; Graudins, Andis; Bailey, Benoit; Mégarbane, Bruno; Calello, Diane P; Levine, Michael; Stellpflug, Samuel J; Hoegberg, Lotte C G; Chuang, Ryan; Stork, Christine; Bhalla, Ashish; Rollins, Carol J; Lavergne, Valéry

2015-07-01

Intravenous lipid emulsion (ILE) therapy is a novel treatment that was discovered in the last decade. Despite unclear understanding of its mechanisms of action, numerous and diverse publications attested to its clinical use. However, current evidence supporting its use is unclear and recommendations are inconsistent. To assist clinicians in decision-making, the American Academy of Clinical Toxicology created a workgroup composed of international experts from various clinical specialties, which includes representatives of major clinical toxicology associations. Rigorous methodology using the Appraisal of Guidelines for Research and Evaluation or AGREE II instrument was developed to provide a framework for the systematic reviews for this project and to formulate evidence-based recommendations on the use of ILE in poisoning. Systematic reviews on the efficacy of ILE in local anesthetic toxicity and non-local anesthetic poisonings as well as adverse effects of ILE are planned. A comprehensive review of lipid analytical interferences and a survey of ILE costs will be developed. The evidence will be appraised using the GRADE system. A thorough and transparent process for consensus statements will be performed to provide recommendations, using a modified Delphi method with two rounds of voting. This process will allow for the production of useful practice recommendations for this therapy.

How to Get the Recommender Out of the Lab?

NASA Astrophysics Data System (ADS)

Picault, Jérome; Ribière, Myriam; Bonnefoy, David; Mercer, Kevin

A personalised system is a complex system made of many interacting parts, from data ingestion to presenting the results to the users. A plethora of methods, tools, algorithms and approaches exist for each piece of such a system: many data and metadata processing methods, many user models, many filtering techniques, many accuracy metrics, many personalisation levels. In addition, a realworld recommender is a piece of an even larger and more complex environment on which there is little control: often the recommender is part of a larger application introducing constraints for the design of the recommender, e.g. the data may not be in a suitable format, or the environment may impose some architectural or privacy constraints. This can make the task of building such a recommender system daunting, and it is easy to make errors. Based on the experience of the authors and the study of other works, this chapter intends to be a guide on the design, implementation and evaluation of personalised systems. It presents the different aspects that must be studied before the design is even started, and how to avoid pitfalls, in a hands-on approach. The chapter presents the main factors to take into account to design a recommender system, and illustrates them through case studies of existing systems to help navigate in the many and complex choices that have to be faced.

Long-term Mechanical Circulatory Support System reliability recommendation by the National Clinical Trial Initiative subcommittee.

PubMed

Lee, James

2009-01-01

The Long-Term Mechanical Circulatory Support (MCS) System Reliability Recommendation was published in the American Society for Artificial Internal Organs (ASAIO) Journal and the Annals of Thoracic Surgery in 1998. At that time, it was stated that the document would be periodically reviewed to assess its timeliness and appropriateness within 5 years. Given the wealth of clinical experience in MCS systems, a new recommendation has been drafted by consensus of a group of representatives from the medical community, academia, industry, and government. The new recommendation describes a reliability test methodology and provides detailed reliability recommendations. In addition, the new recommendation provides additional information and clinical data in appendices that are intended to assist the reliability test engineer in the development of a reliability test that is expected to give improved predictions of clinical reliability compared with past test methods. The appendices are available for download at the ASAIO journal web site at www.asaiojournal.com.

Utility-Based Link Recommendation in Social Networks

ERIC Educational Resources Information Center

Li, Zhepeng

2013-01-01

Link recommendation, which suggests links to connect currently unlinked users, is a key functionality offered by major online social networking platforms. Salient examples of link recommendation include "people you may know"' on Facebook and "who to follow" on Twitter. A social networking platform has two types of stakeholder:…

A recommendation module to help teachers build courses through the Moodle Learning Management System

NASA Astrophysics Data System (ADS)

Limongelli, Carla; Lombardi, Matteo; Marani, Alessandro; Sciarrone, Filippo; Temperini, Marco

2016-01-01

In traditional e-learning, teachers design sets of Learning Objects (LOs) and organize their sequencing; the material implementing the LOs could be either built anew or adopted from elsewhere (e.g. from standard-compliant repositories) and reused. This task is applicable also when the teacher works in a system for personalized e-learning. In this case, the burden actually increases: for instance, the LOs may need adaptation to the system, through additional metadata. This paper presents a module that gives some support to the operations of retrieving, analyzing, and importing LOs from a set of standard Learning Objects Repositories, acting as a recommending system. In particular, it is designed to support the teacher in the phases of (i) retrieval of LOs, through a keyword-based search mechanism applied to the selected repositories; (ii) analysis of the returned LOs, whose information is enriched by a concept of relevance metric, based on both the results of the searching operation and the data related to the previous use of the LOs in the courses managed by the Learning Management System; and (iii) LO importation into the course under construction.

Exploring Long-Term Behavior Patterns in a Book Recommendation System for Reading

ERIC Educational Resources Information Center

Chien, Tzu-Chao; Chen, Zhi-Hong; Chan, Tak-Wai

2017-01-01

This study explored the behavior of students who used a book recommendation system, specifically the My-Bookstore system, over a five semester period. This study addressed two main research questions, the first being related to "the most frequent behaviors and behavioral patterns." The results showed that "visiting" behavior…

A Network and Visual Quality Aware N-Screen Content Recommender System Using Joint Matrix Factorization

PubMed Central

Ullah, Farman; Sarwar, Ghulam; Lee, Sungchang

2014-01-01

We propose a network and visual quality aware N-Screen content recommender system. N-Screen provides more ways than ever before to access multimedia content through multiple devices and heterogeneous access networks. The heterogeneity of devices and access networks present new questions of QoS (quality of service) in the realm of user experience with content. We propose, a recommender system that ensures a better visual quality on user's N-screen devices and the efficient utilization of available access network bandwidth with user preferences. The proposed system estimates the available bandwidth and visual quality on users N-Screen devices and integrates it with users preferences and contents genre information to personalize his N-Screen content. The objective is to recommend content that the user's N-Screen device and access network are capable of displaying and streaming with the user preferences that have not been supported in existing systems. Furthermore, we suggest a joint matrix factorization approach to jointly factorize the users rating matrix with the users N-Screen device similarity and program genres similarity. Finally, the experimental results show that we also enhance the prediction and recommendation accuracy, sparsity, and cold start issues. PMID:24982999

Recommendation in evolving online networks

NASA Astrophysics Data System (ADS)

Hu, Xiao; Zeng, An; Shang, Ming-Sheng

2016-02-01

Recommender system is an effective tool to find the most relevant information for online users. By analyzing the historical selection records of users, recommender system predicts the most likely future links in the user-item network and accordingly constructs a personalized recommendation list for each user. So far, the recommendation process is mostly investigated in static user-item networks. In this paper, we propose a model which allows us to examine the performance of the state-of-the-art recommendation algorithms in evolving networks. We find that the recommendation accuracy in general decreases with time if the evolution of the online network fully depends on the recommendation. Interestingly, some randomness in users' choice can significantly improve the long-term accuracy of the recommendation algorithm. When a hybrid recommendation algorithm is applied, we find that the optimal parameter gradually shifts towards the diversity-favoring recommendation algorithm, indicating that recommendation diversity is essential to keep a high long-term recommendation accuracy. Finally, we confirm our conclusions by studying the recommendation on networks with the real evolution data.

Applying the WHO recommendations on health-sector response to violence against women to assess the Spanish health system. A mixed methods approach.

PubMed

Goicolea, Isabel; Vives-Cases, Carmen; Minvielle, Fauhn; Briones-Vozmediano, Erica; Ohman, Ann

2014-01-01

This methodological note describes the development and application of a mixed-methods protocol to assess the responsiveness of Spanish health systems to violence against women in Spain, based on the World Health Organization (WHO) recommendations. Five areas for exploration were identified based on the WHO recommendations: policy environment, protocols, training, accountability/monitoring, and prevention/promotion. Two data collection instruments were developed to assess the situation of 17 Spanish regional health systems (RHS) with respect to these areas: 1) a set of indicators to guide a systematic review of secondary sources, and 2) an interview guide to be used with 26 key informants at the regional and national levels. We found differences between RHSs in the five areas assessed. The progress of RHSs on the WHO recommendations was notable at the level of policies, moderate in terms of health service delivery, and very limited in terms of preventive actions. Using a mixed-methods approach was useful for triangulation and complementarity during instrument design, data collection and interpretation. Copyright © 2013 SESPAS. Published by Elsevier Espana. All rights reserved.

Factors influencing intentions to use social recommender systems: a social exchange perspective.

PubMed

Chang, Tsung-Sheng; Hsiao, Wei-Hung

2013-05-01

This study employs the perspective of social exchange theory and seeks to understand users' intentions to use social recommender systems (SRS) through three psychological factors: trust, shared values, and reputation. We use structural equation modeling to analyze 221 valid questionnaires. The results show that trust has a direct positive influence on the intention to use SRS, followed by shared values, whereas reputation has an indirect influence on SRS use. We further discuss specific recommendations concerning these factors for developing SRS.

Evidence-based recommendations on the use of intravenous lipid emulsion therapy in poisoning.

PubMed

Gosselin, Sophie; Hoegberg, Lotte C G; Hoffman, Robert S; Graudins, Andis; Stork, Christine M; Thomas, Simon H L; Stellpflug, Samuel J; Hayes, Bryan D; Levine, Michael; Morris, Martin; Nesbitt-Miller, Andrea; Turgeon, Alexis F; Bailey, Benoit; Calello, Diane P; Chuang, Ryan; Bania, Theodore C; Mégarbane, Bruno; Bhalla, Ashish; Lavergne, Valéry

2016-12-01

Although intravenous lipid emulsion (ILE) was first used to treat life-threatening local anesthetic (LA) toxicity, its use has expanded to include both non-local anesthetic (non-LA) poisoning and less severe manifestations of toxicity. A collaborative workgroup appraised the literature and provides evidence-based recommendations for the use of ILE in poisoning. Following a systematic review of the literature, data were summarized in four publications: LA and non-LA poisoning efficacy, adverse effects, and analytical interferences. Twenty-two toxins or toxin categories and three clinical situations were selected for voting. Voting statements were proposed using a predetermined format. A two-round modified Delphi method was used to reach consensus on the voting statements. Disagreement was quantified using RAND/UCLA Appropriateness Method. For the management of cardiac arrest, we recommend using ILE with bupivacaine toxicity, while our recommendations are neutral regarding its use for all other toxins. For the management of life-threatening toxicity, (1) as first line therapy, we suggest not to use ILE with toxicity from amitriptyline, non-lipid soluble beta receptor antagonists, bupropion, calcium channel blockers, cocaine, diphenhydramine, lamotrigine, malathion but are neutral for other toxins, (2) as part of treatment modalities, we suggest using ILE in bupivacaine toxicity if other therapies fail, but are neutral for other toxins, (3) if other therapies fail, we recommend ILE for bupivacaine toxicity and we suggest using ILE for toxicity due to other LAs, amitriptyline, and bupropion, but our recommendations are neutral for all other toxins. In the treatment of non-life-threatening toxicity, recommendations are variable according to the balance of expected risks and benefits for each toxin. For LA-toxicity we suggest the use of Intralipid ® 20% as it is the formulation the most often reported. There is no evidence to support a recommendation for the best

Ultrasonography and the Ultrasound-Based Management of Thyroid Nodules: Consensus Statement and Recommendations

PubMed Central

Baek, Jung Hwan; Jung, So Lyung; Kim, Dong Wook; Kim, Eun Kyung; Kim, Ji Young; Kwak, Jin Young; Lee, Jeong Hyun; Lee, Joon Hyung; Lee, Young Hen; Na, Dong Gyu; Park, Jeong Seon; Park, Sun Won

2011-01-01

The detection of thyroid nodules has become more common with the widespread use of ultrasonography (US). US is the mainstay for detecting and making the differential diagnosis of thyroid nodules as well as for providing guidance for a biopsy. The Task Force on Thyroid Nodules of the Korean Society of Thyroid Radiology has developed recommendations for the US diagnosis and US-based management of thyroid nodules. The review and recommendations in this report have been based on a comprehensive analysis of the current literature, the results of multicenter studies and from the consensus of experts. PMID:21228935

Clustering recommendations to compute agent reputation

NASA Astrophysics Data System (ADS)

Bedi, Punam; Kaur, Harmeet

2005-03-01

Traditional centralized approaches to security are difficult to apply to multi-agent systems which are used nowadays in e-commerce applications. Developing a notion of trust that is based on the reputation of an agent can provide a softer notion of security that is sufficient for many multi-agent applications. Our paper proposes a mechanism for computing reputation of the trustee agent for use by the trustier agent. The trustier agent computes the reputation based on its own experience as well as the experience the peer agents have with the trustee agents. The trustier agents intentionally interact with the peer agents to get their experience information in the form of recommendations. We have also considered the case of unintentional encounters between the referee agents and the trustee agent, which can be directly between them or indirectly through a set of interacting agents. The clustering is done to filter off the noise in the recommendations in the form of outliers. The trustier agent clusters the recommendations received from referee agents on the basis of the distances between recommendations using the hierarchical agglomerative method. The dendogram hence obtained is cut at the required similarity level which restricts the maximum distance between any two recommendations within a cluster. The cluster with maximum number of elements denotes the views of the majority of recommenders. The center of this cluster represents the reputation of the trustee agent which can be computed using c-means algorithm.

A study of concept-based similarity approaches for recommending program examples

NASA Astrophysics Data System (ADS)

Hosseini, Roya; Brusilovsky, Peter

2017-07-01

This paper investigates a range of concept-based example recommendation approaches that we developed to provide example-based problem-solving support in the domain of programming. The goal of these approaches is to offer students a set of most relevant remedial examples when they have trouble solving a code comprehension problem where students examine a program code to determine its output or the final value of a variable. In this paper, we use the ideas of semantic-level similarity-based linking developed in the area of intelligent hypertext to generate examples for the given problem. To determine the best-performing approach, we explored two groups of similarity approaches for selecting examples: non-structural approaches focusing on examples that are similar to the problem in terms of concept coverage and structural approaches focusing on examples that are similar to the problem by the structure of the content. We also explored the value of personalized example recommendation based on student's knowledge levels and learning goal of the exercise. The paper presents concept-based similarity approaches that we developed, explains the data collection studies and reports the result of comparative analysis. The results of our analysis showed better ranking performance of the personalized structural variant of cosine similarity approach.

Personalized professional content recommendation

DOEpatents

Xu, Songhua

2015-10-27

A personalized content recommendation system includes a client interface configured to automatically monitor a user's information data stream transmitted on the Internet. A hybrid contextual behavioral and collaborative personal interest inference engine resident to a non-transient media generates automatic predictions about the interests of individual users of the system. A database server retains the user's personal interest profile based on a plurality of monitored information. The system also includes a server programmed to filter items in an incoming information stream with the personal interest profile and is further programmed to identify only those items of the incoming information stream that substantially match the personal interest profile.

Sensitivity Analysis in Sequential Decision Models.

PubMed

Chen, Qiushi; Ayer, Turgay; Chhatwal, Jagpreet

2017-02-01

Sequential decision problems are frequently encountered in medical decision making, which are commonly solved using Markov decision processes (MDPs). Modeling guidelines recommend conducting sensitivity analyses in decision-analytic models to assess the robustness of the model results against the uncertainty in model parameters. However, standard methods of conducting sensitivity analyses cannot be directly applied to sequential decision problems because this would require evaluating all possible decision sequences, typically in the order of trillions, which is not practically feasible. As a result, most MDP-based modeling studies do not examine confidence in their recommended policies. In this study, we provide an approach to estimate uncertainty and confidence in the results of sequential decision models. First, we provide a probabilistic univariate method to identify the most sensitive parameters in MDPs. Second, we present a probabilistic multivariate approach to estimate the overall confidence in the recommended optimal policy considering joint uncertainty in the model parameters. We provide a graphical representation, which we call a policy acceptability curve, to summarize the confidence in the optimal policy by incorporating stakeholders' willingness to accept the base case policy. For a cost-effectiveness analysis, we provide an approach to construct a cost-effectiveness acceptability frontier, which shows the most cost-effective policy as well as the confidence in that for a given willingness to pay threshold. We demonstrate our approach using a simple MDP case study. We developed a method to conduct sensitivity analysis in sequential decision models, which could increase the credibility of these models among stakeholders.

A Policy Language for Modelling Recommendations

NASA Astrophysics Data System (ADS)

Abou El Kalam, Anas; Balbiani, Philippe

While current and emergent applications become more and more complex, most of existing security policies and models only consider a yes/no response to the access requests. Consequently, modelling, formalizing and implementing permissions, obligations and prohibitions do not cover the richness of all the possible scenarios. In fact, several applications have access rules with the recommendation access modality. In this paper we focus on the problem of formalizing security policies with recommendation needs. The aim is to provide a generic domain-independent formal system for modelling not only permissions, prohibitions and obligations, but also recommendations. In this respect, we present our logic-based language, the semantics, the truth conditions, our axiomatic as well as inference rules. We also give a representative use case with our specification of recommendation requirements. Finally, we explain how our logical framework could be used to query the security policy and to check its consistency.

An evidence-based recommendation on bed head elevation for mechanically ventilated patients.

PubMed

Niël-Weise, Barbara S; Gastmeier, Petra; Kola, Axel; Vonberg, Ralf P; Wille, Jan C; van den Broek, Peterhans J

2011-01-01

A semi-upright position in ventilated patients is recommended to prevent ventilator-associated pneumonia (VAP) and is one of the components in the Ventilator Bundle of the Institute for Health Care Improvement. This recommendation, however, is not an evidence-based one. A systematic review on the benefits and disadvantages of semi-upright position in ventilated patients was done according to PRISMA guidelines. Then a European expert panel developed a recommendation based on the results of the systematic review and considerations beyond the scientific evidence in a three-round electronic Delphi procedure. Three trials (337 patients) were included in the review. The results showed that it was uncertain whether a 45° bed head elevation was effective or harmful with regard to the occurrence of clinically suspected VAP, microbiologically confirmed VAP, decubitus and mortality, and that it was unknown whether 45° elevation for 24 hours a day increased the risk for thromboembolism or hemodynamic instability. A group of 22 experts recommended elevating the head of the bed of mechanically ventilated patients to a 20 to 45° position and preferably to a ≥ 30° position as long as it does not pose risks or conflicts with other nursing tasks, medical interventions or patients' wishes. Although the review failed to prove clinical benefits of bed head elevation, experts prefer this position in ventilated patients. They made clear that the position of a ventilated patient in bed depended on many determinants. Therefore, given the scientific uncertainty about the benefits and harms of a semi-upright position, this position could only be recommended as the preferred position with the necessary restrictions.

33 CFR 62.63 - Recommendations.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 33 Navigation and Navigable Waters 1 2010-07-01 2010-07-01 false Recommendations. 62.63 Section 62... UNITED STATES AIDS TO NAVIGATION SYSTEM Public Participation in the Aids to Navigation System § 62.63 Recommendations. (a) The public may recommend changes to existing aids to navigation, request new aids or the...

Solar water heating system for a lunar base

NASA Technical Reports Server (NTRS)

Somers, Richard E.; Haynes, R. Daniel

1992-01-01

An investigation of the feasibility of using a solar water heater for a lunar base is described. During the investigation, computer codes were developed to model the lunar base configuration, lunar orbit, and heating systems. Numerous collector geometries, orientation variations, and system options were identified and analyzed. The results indicate that the recommended solar water heater could provide 88 percent of the design load and would not require changes in the overall lunar base design. The system would give a 'safe-haven' water heating capability and use only 7 percent to 10 percent as much electricity as an electric heating system. As a result, a fixed position photovoltaic array can be reduced by 21 sq m.

Gulf of Mexico Helicopter Offshore System Technologies Recommended Development Path

NASA Technical Reports Server (NTRS)

Koenke, Edmund J.; Williams, Larry; Calafa, Caesar

1999-01-01

The National Aeronautics and Space Administration (NASA) Advanced Air Transportation Technologies (AATT) project in cooperation with the Department of Transportation (DOT) Volpe National Transportation Systems Center (VNTSC) contracted with the System Resources Corporation (SRC) for the evaluation of the existing environment and the identification of user and service provider needs in the Gulf of Mexico low-altitude Offshore Sector. The results of this contractor activity are reported in the Gulf of Mexico Helicopter Offshore System Technologies Engineering Needs Assessment. A recommended system design and transition strategy was then developed to satisfy the identified needs within the constraints of the environment. This work, also performed under contract to NASA, is the subject of this report.

Association Rule Analysis for Tour Route Recommendation and Application to Wctsnop

NASA Astrophysics Data System (ADS)

Fang, H.; Chen, C.; Lin, J.; Liu, X.; Fang, D.

2017-09-01

The increasing E-tourism systems provide intelligent tour recommendation for tourists. In this sense, recommender system can make personalized suggestions and provide satisfied information associated with their tour cycle. Data mining is a proper tool that extracting potential information from large database for making strategic decisions. In the study, association rule analysis based on FP-growth algorithm is applied to find the association relationship among scenic spots in different cities as tour route recommendation. In order to figure out valuable rules, Kulczynski interestingness measure is adopted and imbalance ratio is computed. The proposed scheme was evaluated on Wangluzhe cultural tourism service network operation platform (WCTSNOP), where it could verify that it is able to quick recommend tour route and to rapidly enhance the recommendation quality.