Leveraging friend and group information to improve social recommender system

  • Jianshan Sun
  • Rongrong Ying
  • Yuanchun JiangEmail author
  • Jianmin He
  • Zhengping Ding


In recent years, we have witnessed a flourish of social commerce services. Online users can easily share their experiences on products or services with friends. Social recommender systems are employed to tailor right products for user needs. However, existing recommendation methods try to consider the social information to improve the recommendation performance while they do not differ the impact of different social information and do not have deep analysis on social information. In this paper, we propose a social recommendation framework to leverage the friend and group information to extend the traditional BPR model from different perspectives. Through a detailed experiment on LAST.FM data set, we find that the proposed methods are effective in improving the recommendation accuracy and we also have a good understanding for the impact of friend and group information on recommendation performance.


Social recommender system Friend Group Positive feedback 



This work is supported by the National Natural Science Foundation of China (71501057, 71872060, 91846201, 71490725, 71722010, 91546114, 91746302) and the Foundation for Innovative Research Groups of the National Natural Science Foundation of China (71521001), partially Sponsored by Zhejiang Lab (No. 2019KE0AB04).


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Authors and Affiliations

  1. 1.School of ManagementHefei University of TechnologyHefeiChina
  2. 2.Key Laboratory of Process Optimization and Intelligent Decision MakingMinistry of EducationHefeiChina
  3. 3.Ministry of Education Engineering Research Center for Intelligent Decision-Making & Information System TechnologiesHefeiChina

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