Social collaborative filtering by trust

Bo Yang, Yu Lei, Dayou Liu, Jiming Liu

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

196 Citations (Scopus)


To accurately and actively provide users with their potentially interested information or services is the main task of a recommender system. Collaborative filtering is one of the most widely adopted recommender algorithms, whereas it is suffering the issues of data sparsity and cold start that will severely degrade quality of recommendations. To address such issues, this article proposes a novel method, trying to improve the performance of collaborative filtering recommendation by means of elaborately integrating twofold sparse information, the conventional rating data given by users and the social trust network among the same users. It is a model-based method adopting matrix factorization technique to map users into low-dimensional latent feature spaces in terms of their trust relationship, aiming to reflect users' reciprocal influence on their own opinions more reasonably. The validations against a real-world dataset show that the proposed method performs much better than state-of-the-art recommendation algorithms for social collaborative filtering by trust.

Original languageEnglish
Title of host publicationIJCAI 2013 - Proceedings of the 23rd International Joint Conference on Artificial Intelligence
Number of pages7
Publication statusPublished - 2013
Event23rd International Joint Conference on Artificial Intelligence, IJCAI 2013 - Beijing, China, Beijing, China
Duration: 3 Aug 20139 Aug 2013

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
ISSN (Print)1045-0823


Conference23rd International Joint Conference on Artificial Intelligence, IJCAI 2013
Internet address

Scopus Subject Areas

  • Artificial Intelligence


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