ABSTRACT
Recommender Systems have been successfully applied to alleviate the information overload problem and assist the process of decision making. Collaborative filtering, as one of the most popular recommendation algorithms, has been fully explored and developed in the past two decades. However, one of the challenges in collaborative filtering, the problem of "Grey Sheep" user, is still under investigation. "Grey Sheep" users is a group of the users who have special tastes and they may neither agree nor disagree with the majority of the users. The identification of them becomes a challenge in collaborative filtering, since they may introduce difficulties to produce accurate collaborative recommendations. In this paper, we propose a novel approach which can identify the Grey Sheep users by reusing the outlier detection techniques based on the distribution of user-user similarities. Our experimental results based on the MovieLens 10M rating data demonstrate the ease and effectiveness of our proposed approach.
- Gediminas Adomavicius, Bamshad Mobasher, Francesco Ricci, and Alexander Tuzhilin 2011. Context-Aware Recommender Systems. AI Magazine, Vol. 32, 3 (2011), 67--80.Google ScholarCross Ref
- Markus M. Breunig, Hans-Peter Kriegel, Raymond T. Ng, and Jörg Sander 2000. LOF: identifying density-based local outliers. In ACM sigmod record, Vol. 29. ACM, 93--104. Google ScholarDigital Library
- Robin Burke. 2002. Hybrid recommender systems: Survey and experiments. User modeling and user-adapted interaction Vol. 12, 4 (2002), 331--370. Google ScholarDigital Library
- Varun Chandola, Arindam Banerjee, and Vipin Kumar. 2009. Anomaly detection: A survey. ACM computing surveys (CSUR) Vol. 41, 3 (2009), 15. Google ScholarDigital Library
- Mark Claypool, Anuja Gokhale, Tim Miranda, Pavel Murnikov, Dmitry Netes, and Matthew Sartin. 1999. Combining content-based and collaborative filters in an online newspaper Proceedings of ACM SIGIR workshop on recommender systems, Vol. Vol. 60.Google Scholar
- Mustansar Ghazanfar and Adam Prugel-Bennett 2011. Fulfilling the Needs of Gray-Sheep Users in Recommender Systems, A Clustering Solution Proceedings of the 2011 International Conference on Information Systems and Computational Intelligence. 18--20.Google Scholar
- Mustansar Ali Ghazanfar and Adam Prügel-Bennett 2014. Leveraging clustering approaches to solve the gray-sheep users problem in recommender systems. Expert Systems with Applications Vol. 41, 7 (2014), 3261--3275. Google ScholarDigital Library
- Benjamin Gras, Armelle Brun, and Anne Boyer 2016. Identifying Grey Sheep Users in Collaborative Filtering: a Distribution-Based Technique Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization. ACM, 17--26. Google ScholarDigital Library
- Victoria Hodge and Jim Austin 2004. A survey of outlier detection methodologies. Artificial intelligence review Vol. 22, 2 (2004), 85--126. Google ScholarDigital Library
- John McCrae, Anton Piatek, and Adam Langley. 2004. Collaborative filtering. http://www.imperialviolet.org (2004).Google Scholar
- Paul Resnick, Neophytos Iacovou, Mitesh Suchak, Peter Bergstrom, and John Riedl 1994. GroupLens: an open architecture for collaborative filtering of netnews Proceedings of the 1994 ACM conference on Computer supported cooperative work. ACM, 175--186. Google ScholarDigital Library
- Manuela Ruiz-Montiel and José Aldana-Montes 2009. Semantically enhanced recommender systems. In On the move to meaningful internet systems: OTM 2009 workshops. Springer, 604--609. Google ScholarDigital Library
- Xiaoyuan Su and Taghi M. Khoshgoftaar 2009. A survey of collaborative filtering techniques. Advances in artificial intelligence Vol. 2009 (2009), 4. Google ScholarDigital Library
- Y. Zheng, B. Mobasher, and R. Burke 2014. CSLIM: Contextual SLIM Recommendation Algorithms Proceedings of the 8th ACM Conference on Recommender Systems. ACM, 301--304. Google ScholarDigital Library
- Yong Zheng, Bamshad Mobasher, and Robin Burke. 2015. Similarity-Based Context-aware Recommendation. In Proceedings of the 2015 Conference on Web Information Systems Engineering. Springer Berlin Heidelberg, 431--447. Google ScholarDigital Library
Index Terms
- Identifying Grey Sheep Users By The Distribution of User Similarities In Collaborative Filtering
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