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Issue No. 09 - Sept. (2016 vol. 28)
ISSN: 1041-4347
pp: 2363-2375
Jiajun Bu , Zhejiang Provincial Key Laboratory of Service Robot, College of Computer Science, Zhejiang University, Hangzhou, China
Xin Shen , Zhejiang Provincial Key Laboratory of Service Robot, College of Computer Science, Zhejiang University, Hangzhou, China
Bin Xu , Zhejiang Provincial Key Laboratory of Service Robot, College of Computer Science, Zhejiang University, Hangzhou, China
Chun Chen , Zhejiang Provincial Key Laboratory of Service Robot, College of Computer Science, Zhejiang University, Hangzhou, China
Xiaofei He , State Key Lab of CAD&CG, College of Computer Science, Zhejiang University, Hangzhou, China
Deng Cai , State Key Lab of CAD&CG, College of Computer Science, Zhejiang University, Hangzhou, China
ABSTRACT
Collaborative filtering (CF) is out of question the most widely adopted and successful recommendation approach. A typical CF-based recommender system associates a user with a group of like-minded users based on their individual preferences over all the items, either explicit or implicit, and then recommends to the user some unobserved items enjoyed by the group. However, we find that two users with similar tastes on one item subset may have totally different tastes on another set. In other words, there exist many user-item subgroups each consisting of a subset of items and a group of like-minded users on these items. It is more reasonable to predict preferences through one user's correlated subgroups, but not the entire user-item matrix. In this paper, to find meaningful subgroups, we formulate a new Multiclass Co-Clustering (MCoC) model, which captures relations of user-to-item, user-to-user, and item-to-item simultaneously. Then, we combine traditional CF algorithms with subgroups for improving their top- $_$N$_$ recommendation performance. Our approach can be seen as a new extension of traditional clustering CF models. Systematic experiments on several real data sets have demonstrated the effectiveness of our proposed approach.
INDEX TERMS
Collaboration, Recommender systems, Clustering algorithms, Motion pictures, Data models, Prediction algorithms
CITATION

J. Bu, X. Shen, B. Xu, C. Chen, X. He and D. Cai, "Improving Collaborative Recommendation via User-Item Subgroups," in IEEE Transactions on Knowledge & Data Engineering, vol. 28, no. 9, pp. 2363-2375, 2016.
doi:10.1109/TKDE.2016.2566622
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