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Leveraging friend and group information to improve social recommender system

Author

Listed:
  • Jianshan Sun

    (Hefei University of Technology
    Ministry of Education
    Ministry of Education Engineering Research Center for Intelligent Decision-Making & Information System Technologies)

  • Rongrong Ying

    (Hefei University of Technology)

  • Yuanchun Jiang

    (Hefei University of Technology)

  • Jianmin He

    (Hefei University of Technology)

  • Zhengping Ding

    (Hefei University of Technology)

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.

Suggested Citation

  • Jianshan Sun & Rongrong Ying & Yuanchun Jiang & Jianmin He & Zhengping Ding, 2020. "Leveraging friend and group information to improve social recommender system," Electronic Commerce Research, Springer, vol. 20(1), pages 147-172, March.
    • Handle: RePEc:spr:elcore:v:20:y:2020:i:1:d:10.1007_s10660-019-09390-3
    DOI: 10.1007/s10660-019-09390-3
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    References listed on IDEAS

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    1. Xuemei Tian & Libo Liu, 2017. "Does big data mean big knowledge? Integration of big data analysis and conceptual model for social commerce research," Electronic Commerce Research, Springer, vol. 17(1), pages 169-183, March.
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    6. Robert M. Bond & Christopher J. Fariss & Jason J. Jones & Adam D. I. Kramer & Cameron Marlow & Jaime E. Settle & James H. Fowler, 2012. "A 61-million-person experiment in social influence and political mobilization," Nature, Nature, vol. 489(7415), pages 295-298, September.
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    Cited by:

    1. Saravanan Thirumuruganathan & Soon-gyo Jung & Dianne Ramirez Robillos & Joni Salminen & Bernard J. Jansen, 2021. "Forecasting the nearly unforecastable: why aren’t airline bookings adhering to the prediction algorithm?," Electronic Commerce Research, Springer, vol. 21(1), pages 73-100, March.
    2. Ransome Epie Bawack & Emilie Bonhoure, 2023. "Influencer is the New Recommender: insights for Theorising Social Recommender Systems," Information Systems Frontiers, Springer, vol. 25(1), pages 183-197, February.
    3. Weiwei Deng, 2022. "Leveraging consumer behaviors for product recommendation: an approach based on heterogeneous network," Electronic Commerce Research, Springer, vol. 22(4), pages 1079-1105, December.

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