Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (17): 190-195.DOI: 10.3778/j.issn.1002-8331.2005-0316

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Social Recommendation Combined with Important Nodes Trust Propagation

GU Junhua, CHEN Bo, WANG Rui, ZHANG Suqi   

  1. 1.School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China
    2.Hebei Province Key Laboratory of Big Data Computing, Tianjin 300401, China
    3.School of Information Engineering, Tianjin University of Commerce, Tianjin 300134, China
  • Online:2021-09-01 Published:2021-08-30



  1. 1.河北工业大学 人工智能与数据科学学院,天津 300401
    2.河北省大数据计算重点实验室,天津 300401
    3.天津商业大学 信息工程学院,天津 300134


The recommendation algorithm based on social information effectively alleviates the data sparsity and cold start problems in the recommendation algorithm, which has attracted great attention in recent years. However, social information still has the problem of data sparsity, and the binary data provided by social networks can not measure the degree of trust between different users. Therefore, first of all, it uses random walk with restart to obtain important nodes in social network. Then, the important nodes trust propagation algorithm is proposed to establish the trust relationship between indirect user nodes and important nodes. At the same time, the trust weight between users is further quantified by using the structural information of nodes to get more accurate recommendation results. Experiments on three open datasets show that the social recommendation combined with important nodes trust propagation enriches social information and effectively improves the accuracy and recall rate of the recommendation algorithm.

Key words: recommendation algorithm, social information, important nodes, trust propagation, Random Walk with Restart(RWR)



关键词: 推荐算法, 社交信息, 重要节点, 信任传播, 重启随机游走算法