A framework for collaborative filtering recommender systems |
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| Authors: | Jesus Bobadilla Antonio Hernando Fernando Ortega Jesus Bernal |
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| Affiliation: | 1. School of Computer Science, China University of Geosciences, Wuhan 430074, China;2. Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan 430074, China;3. GraphSQL Inc., Mountain View, CA 94043, USA;4. Department of Computer Science, Kent State University, Kent, OH 44240, USA;5. School of Engineering and ICT, University of Tasmania, Hobart, Australia;6. Department of Information Systems and Cyber Security, University of Texas at San Antonio, San Antonio, TX 78249, USA;7. School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, SA 5095, Australia;8. Department of Computer Information Systems, Louisiana Tech University, Ruston, LA 71272, USA;1. Department of Computer Science, University of Tabriz, Tabriz, Iran;1. Faculty of Computing, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Malaysia;2. Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Malaysia;1. Room 418, Building 138, Seoul National University, Sillim-9-dong, Gwanak-gu, Seoul, Republic of Korea;2. Room 206B, Building E8-10, Chungbuk National University, 12 Gaesin-dong, Heungdeok-gu, Cheongju-si, Chungcheongbuk-do, Republic of Korea |
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| Abstract: | As the use of recommender systems becomes more consolidated on the Net, an increasing need arises to develop some kind of evaluation framework for collaborative filtering measures and methods which is capable of not only testing the prediction and recommendation results, but also of other purposes which until now were considered secondary, such as novelty in the recommendations and the users’ trust in these. This paper provides: (a) measures to evaluate the novelty of the users’ recommendations and trust in their neighborhoods, (b) equations that formalize and unify the collaborative filtering process and its evaluation, (c) a framework based on the above-mentioned elements that enables the evaluation of the quality results of any collaborative filtering applied to the desired recommender systems, using four graphs: quality of the predictions, the recommendations, the novelty and the trust. |
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