A Factored Similarity Model with Trust and Social Influence for Top-N Recommendation


  • Xuefeng Zhang School of Computer Science and Technology, Hangzhou Dianzi University Hangzhou 310018, China
  • Xiuli Chen School of Computer Science and Technology, Hangzhou Dianzi University Hangzhou 310018, China
  • Dewen Seng School of Computer Science and Technology, Hangzhou Dianzi University Hangzhou 310018, China
  • Xujian Fang School of Computer Science and Technology, Hangzhou Dianzi University Hangzhou 310018, China


recommendation system, matrix factorization, trust, social influence, deep learning, top-n recommendation


Many trust-aware recommendation systems have emerged to overcome the problem of data sparsity, which bottlenecks the performance of traditional Collaborative Filtering (CF) recommendation algorithms. However, these systems most rely on the binary social network information, failing to consider the variety of trust values between users. To make up for the defect, this paper designs a novel Top-N recommendation model based on trust and social influence, in which the most influential users are determined by the Improved Structural Holes (ISH) method. Specifically, the features in Matrix Factorization (MF) were configured by deep learning rather than random initialization, which has a negative impact on prediction of item rating. In addition, a trust measurement model was created to quantify the strength of implicit trust. The experimental result shows that our approach can solve the adverse impacts of data sparsity and enhance the recommendation accuracy.


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