Journal
JOURNAL OF SYSTEMS AND SOFTWARE
Volume 99, Issue -, Pages 109-119Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.jss.2014.09.019
Keywords
Recommender system; Social network; Social-based recommender system
Funding
- City University of Hong Kong [7004051]
- National Natural Science Foundation of China [60571048, 60873264, 60971088]
- Qing Lan Project
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The traditional recommender systems, especially the collaborative filtering recommender systems, have been studied by many researchers in the past decade. However, they ignore the social relationships among users. In fact, these relationships can improve the accuracy of recommendation. In recent years, the study of social-based recommender systems has become an active research topic. In this paper, we propose a social regularization approach that incorporates social network information to benefit recommender systems. Both users' friendships and rating records (tags) are employed to predict the missing values (tags) in the user-item matrix. Especially, we use a biclustering algorithm to identify the most suitable group of friends for generating different final recommendations. Empirical analyses on real datasets show that the proposed approach achieves superior performance to existing approaches. (C) 2014 Elsevier Inc. All rights reserved.
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