Leveraging friend and group information to improve social recommender system

Jianshan Sun, Rongrong Ying, Yuanchun Jiang (), Jianmin He and Zhengping Ding
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Jianshan Sun: Hefei University of Technology
Rongrong Ying: Hefei University of Technology
Yuanchun Jiang: Hefei University of Technology
Jianmin He: Hefei University of Technology
Zhengping Ding: Hefei University of Technology

Electronic Commerce Research, 2020, vol. 20, issue 1, No 7, 147-172

Abstract: Abstract 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.

Keywords: Social recommender system; Friend; Group; Positive feedback (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10660-019-09390-3

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