A Factored Similarity Model with Trust and Social Influence for Top-N Recommendation
作者: Xuefeng ZhangXiuli ChenDewen SengXujian Fang
刊名: International Journal of Computers Communications & Control, 2019, Vol.14 (4), pp.590-607
来源数据库: Agora University
DOI: 10.15837/ijccc.2019.4.3577
关键词: Recommendation systemMatrix factorizationTrustSocial influenceDeep learningTop-n recommendation
原始语种摘要: 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...
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  • trust 相信
  • recommendation 建议
  • factorization 因式分解
  • users 使用者
  • features 特征
  • initialization 初始化
  • learning 学识
  • influence 影响
  • quantify 量化
  • result 成果