Recommender Systems: An Introduction

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Peer J Computer Science. Collective intelligence as mechanism of medical diagnosis: The iPixel approach. Expert Systems with Applications. Sophatsathit N, editor. Hot news recommendation system from heterogeneous websites based on bayesian model. The Scientific World J. Towards collaborative filtering recommender systems for tailored health communications. An intelligent recommender system based on short-term risk prediction for heart disease patients. Recommender system for personalised wellness therapy.

Li J, Zaman N, editors. Chulyadyo R, Leray P. Procedia Computer Science. Factorization forecasting approach for user modeling. J Comput Sci Cybern. Bedi P, Agarwal SK. Aspect-Oriented trust based mobile recommender system. Using past-prediction accuracy in recommender systems. Information Sciences. Personalizing trip recommendations: A framework proposal. Global Journal of Computer Science. A model to represent users trust in recommender systems using ontologies and fuzzy linguistic modeling. Son L, Thong NT. Intuitionistic fuzzy recommender systems: an effective tool for medical diagnosis.

Knowledge-Based Systems. A nursing care plan recommender system using a data mining approach. A cloud based health insurance plan recommendation system: A user centered approach. Future Generation Computer Systems. An ontology-based recommender system to promote physical activity for pre-frail elderly.

J biomedical informatics. Geetha K, Manimekalai M. Wiesner M, Pfeifer D. Health recommender systems: concepts, requirements, technical basics and challenges. International journal of environmental research and public health. A distributed collaborative platform for personal health profiles in patient-driven health social network.

Introduction to Recommender Systems with Joseph A Konstan and Michael D Ekstrand

International Journal of Distributed Sensor Networks. Patient portal preferences: perspectives on imaging information. Journal of the Association for Information Science and Technology. Reducing errors through a web-based self-management support system. Stud Health Technol Inform. Why do patients and caregivers seek answers from the internet and online lung specialists?

A qualitative study. J medical Internet research. Survey on the worldwide Chronic Myeloid Leukemia Advocates Network regarding complementary and alternative medicine. J cancer research and clinical oncology.

Recommender Systems: Introduction and Challenges

Using Web-based interventions to support caregivers of patients with cancer: a systematic review. Oncol Nurs Forum. Translating research into practice: Evaluation of an e-learning resource for health care professionals to provide nutrition advice and support for cancer survivors. Nurse education today. Shaw R, Thomas R. The information needs and media preferences of Canadian cancer specialists regarding breast cancer treatment related arm morbidity. European J cancer care. An efficient and scalable recommender system for the smart web. Move2Play: an innovative approach to encouraging people to be more physically active.

Social network analysis applied to recommendation systems: alleviating the cold-user problem.

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International Research Journal of Engineering and Technology. Collaborative personalized web recommender system using entropy based similarity measure. International Journal of Computer Science Issues. A systematic literature review on Health Recommender Systems. Suguna R, Sharmila D. An efficient web recommendation system using collaborative filtering and pattern discovery algorithms.

International Journal of Computer Applications. Management Science Letters. Dubey P, Nair PS. Munshi A, Tanna S. International Journal of Engineering Development and Research. Evaluation is important in assessing the effectiveness of recommendation algorithms. The commonly used metrics are the mean squared error and root mean squared error , the latter having been used in the Netflix Prize.

The information retrieval metrics such as precision and recall or DCG are useful to assess the quality of a recommendation method. Diversity, novelty, and coverage are also considered as important aspects in evaluation. User studies are rather small scale. A few dozens or hundreds of users are presented recommendations created by different recommendation approaches, and then the users judge which recommendations are best.

The effectiveness is measured with implicit measures of effectiveness such as conversion rate or click-through rate.

Offline evaluations are based on historic data, e. The effectiveness of recommendation approaches is then measured based on how well a recommendation approach can predict the users' ratings in the dataset. While a rating is an explicit expression of whether a user liked a movie, such information is not available in all domains.

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For instance, in the domain of citation recommender systems, users typically do not rate a citation or recommended article. In such cases, offline evaluations may use implicit measures of effectiveness. For instance, it may be assumed that a recommender system is effective that is able to recommend as many articles as possible that are contained in a research article's reference list. However, this kind of offline evaluations is seen critical by many researchers.

Typically, research on recommender systems is concerned about finding the most accurate recommendation algorithms. However, there are a number of factors that are also important. Previous research has had little impact on the practical application of recommender systems. By , Ekstrand, Konstan, et al. They conclude that seven actions are necessary to improve the current situation: [79] " 1 survey other research fields and learn from them, 2 find a common understanding of reproducibility, 3 identify and understand the determinants that affect reproducibility, 4 conduct more comprehensive experiments 5 modernize publication practices, 6 foster the development and use of recommendation frameworks, and 7 establish best-practice guidelines for recommender-systems research.

From Wikipedia, the free encyclopedia. This article needs additional citations for verification. Please help improve this article by adding citations to reliable sources. Unsourced material may be challenged and removed. Parts of this article those related to documentation need to be updated. Please update this article to reflect recent events or newly available information.

April Main article: Collaborative filtering. Further information: Location based recommendation. Main article: Netflix Prize. Rating site Cold start Collaborative filtering Collective intelligence Content discovery platform Enterprise bookmarking Filter bubble Personalized marketing Preference elicitation Product finder Configurator Pattern recognition.

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Retrieved 1 June Chen, A. Ororbia II, C. Chen, L. Gou, X. Zhang, C. Sim and R. Roy Content-based book recommendation using learning for text categorization. In Workshop Recom. Recommender Systems Handbook 2 ed. Springer US. Computer Science Review. Schein, Alexandrin Popescul, Lyle H. Ungar , David M. Pennock Methods and Metrics for Cold-Start Recommendations.

Retrieved Retrieved 27 October ACM, June Artificial Intelligence Review.

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January ACM Trans. October International Journal on Digital Libraries. Journal of Scientometric Research. Patent 7,,, issued May 22, Patent 7,,, issued January 27, Patent 8,,, issued November 8, Snodgrass, and Joel R.

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Patent 8,,, issued June 18, Patent 9,,, issued June 30, Empirical analysis of predictive algorithms for collaborative filtering. Recommender Systems:An Introduction. Archived from the original on Microsoft Research. Information Sciences. International J. Man-Machine Studies. July Recommender Systems: The Textbook. The Adaptive Web.

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American Mathematical Society. Retrieved October 31, Archived from the original PDF on Pazzani Journal of Information Science. The New York Times. Bell; Y. Koren; C. Volinsky Netflix Prize Forum. User Modeling, Adaptation, and Personalization. Lecture Notes in Computer Science. Springer Berlin Heidelberg. SIGIR ' Research and Advanced Technology for Digital Libraries.

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