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Recommender Systems: Introduction and Challenges
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Social network analysis applied to recommendation systems: alleviating the cold-user problem.
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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.
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:  " 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.
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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.
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.
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Introduction to recommender systems – Mango Solutions
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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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