Combining Trust Propagation and Topic-Level User Interest Expansion in Recommender Systems

Zukun Yu, William Wei Song, Xiaolin Zheng and Deren Chen
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Zukun Yu: Computer Science College, Zhejiang University, Hangzhou, China
William Wei Song: School of Technology and Business Studies, University of Dalarna Borlänge, Sweden
Xiaolin Zheng: Computer Science College, Zhejiang University, Hangzhou, China
Deren Chen: Computer Science College, Zhejiang University, Hangzhou, China

International Journal of Web Services Research (IJWSR), 2016, vol. 13, issue 2, 1-19

Abstract: With the development of E-commerce and Internet, items are becoming more and more, which brings a so called information overload problem that it is hard for users to find the items they would be interested in. Recommender systems emerge to response to this problem through discovering user interest based on their rating information automatically. But the rating information is usually sparse compared to all the possible ratings between users and items. Therefore, it is hard to find out user interest, which is the most important part in recommender systems. In this paper, we propose a recommendation method TT-Rec that employs trust propagation and topic-level user interest expansion to predict user interest. TT-Rec uses a reputation-based method to weight users' influence on other users when propagating trust. TT-Rec also considers discovering user interest by expanding user interest in topic level. In the evaluation, we use three metrics MAE, Coverage and F1 to evaluate TT-Rec through comparative experiments. The experiment results show that TT-Rec recommendation method has a good performance.

Date: 2016
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