Leveraging clustering approaches to solve the gray-sheep users problem in recommender systems |
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| Affiliation: | 1. Department of Software Engineering, Faculty of Telecommunication and Information Engineering, University of Engineering and Technology, Taxila, Pakistan;2. School of Electronics and Computer Science, University of Southampton, Highfield Campus, Southampton SO17 1BJ, United Kingdom;1. Department of Electronics Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong Special Administrative Region;2. Department of Computer Science and Technology, Soochow University, Suzhou 215006, China;1. Department of Information Management at Fortune Institute of Technology, Kaohsiung, Taiwan;2. Thecus Technology Corporation, Taiwan;3. Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan;1. Department of Statistics, Cheongju University, 298, Daeseong-ro Sangdang-gu, Cheongju, Chungbuk 360-764, Republic of Korea;2. Graduate School of Management of Technology, Korea University, 1, 5-Ka, Anam-dong Sungbuk-ku, Seoul 136-701, Republic of Korea;3. Division of Industrial Management Engineering, Korea University, 1, 5-Ka, Anam-dong Sungbuk-ku, Seoul 136-701, Republic of Korea;1. Chongqing Key Lab of Mobile Communications Technology, Chongqing University of Posts and Telecommunications, Chongqing, China;2. Graduate Telecommunications and Networking Program, University of Pittsburgh, PA, USA;3. China Internet Research Lab, China Science and Technology Network, Computer Network Information Center, Chinese Academy of Sciences, Beijing, China;4. Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, China;1. Department of Business Administration, Lunghwa University of Science and Technology, Taiwan;2. Department of Finance, MingDao University, Taiwan;3. Business School, the University of Nottingham, United Kingdom |
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| Abstract: | Recommender systems apply data mining and machine learning techniques for filtering unseen information and can predict whether a user would like a given item. This paper focuses on gray-sheep users problem responsible for the increased error rate in collaborative filtering based recommender systems. This paper makes the following contributions: we show that (1) the presence of gray-sheep users can affect the performance – accuracy and coverage – of the collaborative filtering based algorithms, depending on the data sparsity and distribution; (2) gray-sheep users can be identified using clustering algorithms in offline fashion, where the similarity threshold to isolate these users from the rest of community can be found empirically. We propose various improved centroid selection approaches and distance measures for the K-means clustering algorithm; (3) content-based profile of gray-sheep users can be used for making accurate recommendations. We offer a hybrid recommendation algorithm to make reliable recommendations for gray-sheep users. To the best of our knowledge, this is the first attempt to propose a formal solution for gray-sheep users problem. By extensive experimental results on two different datasets (MovieLens and community of movie fans in the FilmTrust website), we showed that the proposed approach reduces the recommendation error rate for the gray-sheep users while maintaining reasonable computational performance. |
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| Keywords: | Gray-sheep users Recommender systems Collaborative filtering K-Means clustering |
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