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Social Interest For User Selecting Items In Recommender Systems

Author

Listed:
  • DA-CHENG NIE

    (Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China)

  • MING-JING DING

    (Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China)

  • YAN FU

    (Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China)

  • JUN-LIN ZHOU

    (Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China)

  • ZI-KE ZHANG

    (Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China;
    Institute of Information Economy, Hangzhou Normal University, Hangzhou 310036, P. R. China)

Abstract

Recommender systems have developed rapidly and successfully. The system aims to help users find relevant items from a potentially overwhelming set of choices. However, most of the existing recommender algorithms focused on the traditional user-item similarity computation, other than incorporating the social interests into the recommender systems. As we know, each user has their own preference field, they may influence their friends' preference in their expert field when considering the social interest on their friends' item collecting. In order to model this social interest, in this paper, we proposed a simple method to compute users' social interest on the specific items in the recommender systems, and then integrate this social interest with similarity preference. The experimental results on two real-world datasetsEpinionsandFriendfeedshow that this method can significantly improve not only the algorithmic precision-accuracy but also the diversity-accuracy.

Suggested Citation

  • Da-Cheng Nie & Ming-Jing Ding & Yan Fu & Jun-Lin Zhou & Zi-Ke Zhang, 2013. "Social Interest For User Selecting Items In Recommender Systems," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 24(04), pages 1-11.
    • Handle: RePEc:wsi:ijmpcx:v:24:y:2013:i:04:n:s0129183113500228
    DOI: 10.1142/S0129183113500228
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    Cited by:

    1. Guo, Xin-Yu & Guo, Qiang & Li, Ren-De & Liu, Jian-Guo, 2018. "Long-term memory of rating behaviors for the online trust formation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 254-264.

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