Social Collaborative Filtering by Trust

Bo Yang, Yu Lei, Jiming LIU, Wenjie Li

Research output: Contribution to journalArticlepeer-review

197 Citations (Scopus)

Abstract

Recommender systems are used to accurately and actively provide users with potentially interesting information or services. Collaborative filtering is a widely adopted approach to recommendation, but sparse data and cold-start users are often barriers to providing high quality recommendations. To address such issues, we propose a novel method that works to improve the performance of collaborative filtering recommendations by integrating sparse rating data given by users and sparse social trust network among these same users. This is a model-based method that adopts matrix factorization technique that maps users into low-dimensional latent feature spaces in terms of their trust relationship, and aims to more accurately reflect the users reciprocal influence on the formation of their own opinions and to learn better preferential patterns of users for high-quality recommendations. We use four large-scale datasets to show that the proposed method performs much better, especially for cold start users, than state-of-the-art recommendation algorithms for social collaborative filtering based on trust.

Original languageEnglish
Pages (from-to)1633-1647
Number of pages15
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume39
Issue number8
DOIs
Publication statusPublished - 1 Aug 2017

Scopus Subject Areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

User-Defined Keywords

  • collaborative filtering
  • matrix factorization
  • Recommender system
  • trust network

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