ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2016, Vol. 53 ›› Issue (6): 1389-1399.doi: 10.7544/issn1000-1239.2016.20150307

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A Collaborative Filtering Recommendation Algorithm Based on Multiple Social Trusts

Wang Ruiqin1, Jiang Yunliang1, Li Yixiao2, Lou Jungang1   

  1. 1(School of Information Engineering, Huzhou University, Huzhou, Zhejiang 313000);2(School of Information Management and Engineering, Zhejiang University of Finance & Economics, Hangzhou 310018)
  • Online:2016-06-01

Abstract: Collaborative filtering (CF) is one of the most successful recommendation technologies in the personalized recommendation systems. It can recommend products or information for target user according to the preference information of similar users. However the traditional collaborative filtering algorithms have the disadvantages of low recommendation efficiency and weak capacity of attack-resistance. In order to solve the above problems, a novel collaborative filtering algorithm based on social trusts is proposed. Firstly, referring to the trust generation principle in social psychology, a social trust computation method based on multiple trust elements is presented. In social networking environment, trust elements mainly include credibility, reliability, intimacy and self-orientation. Then specific methods of identifying, extraction and quantification of the trust elements are studied in depth. Finally, the trustworthy neighbors of target user are selected in accordance with the social trust, so as to make trust-based collaborative recommendation. Using the FilmTrust and Epinions as test data sets, the performance of the novel algorithm is compared with that of the traditional CF and the-state-of-art methods, as well as the CF based on single trust element. Experimental results show that compared with the other methods, the proposed algorithm not only improves the recommendation precision and recall, but also has powerful attack-resistance capacity.

Key words: collaborative filtering (CF), social network, trust, trust elements, recommendation precision, recall, attack-resistance capacity

CLC Number: