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Effects Of User'S Tastes On Personalized Recommendation

Author

Listed:
  • JIAN-GUO LIU

    (Research Center of Complex Systems Science, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China;
    Department of Modern Physics, University of Science and Technology of China, Hefei 230026, P. R. China;
    Department of Physics, University of Fribourg, Fribourg CH-1700, Switzerland)

  • TAO ZHOU

    (Research Center of Complex Systems Science, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China;
    Department of Modern Physics, University of Science and Technology of China, Hefei 230026, P. R. China;
    Department of Physics, University of Fribourg, Fribourg CH-1700, Switzerland)

  • BING-HONG WANG

    (Research Center of Complex Systems Science, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China;
    Department of Modern Physics, University of Science and Technology of China, Hefei 230026, P. R. China;
    Department of Physics, University of Fribourg, Fribourg CH-1700, Switzerland)

  • YI-CHENG ZHANG

    (Research Center of Complex Systems Science, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China;
    Department of Modern Physics, University of Science and Technology of China, Hefei 230026, P. R. China;
    Department of Physics, University of Fribourg, Fribourg CH-1700, Switzerland)

  • QIANG GUO

    (School Business, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China)

Abstract

In this paper, based on a weighted projection of the user-object bipartite network, we study the effects of user tastes on the mass-diffusion-based personalized recommendation algorithm, where a user's tastes or interests are defined by the average degree of the objects he has collected. We argue that the initial recommendation power located on the objects should be determined by both of their degree and the user's tastes. By introducing a tunable parameter, the user taste effects on the configuration of initial recommendation power distribution are investigated. The numerical results indicate that the presented algorithm could improve the accuracy, measured by the average ranking score. More importantly, we find that when the data is sparse, the algorithm should give more recommendation power to the objects whose degrees are close to the user's tastes, while when the data becomes dense, it should assign more power on the objects whose degrees are significantly different from user's tastes.

Suggested Citation

  • Jian-Guo Liu & Tao Zhou & Bing-Hong Wang & Yi-Cheng Zhang & Qiang Guo, 2009. "Effects Of User'S Tastes On Personalized Recommendation," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 20(12), pages 1925-1932.
    • Handle: RePEc:wsi:ijmpcx:v:20:y:2009:i:12:n:s0129183109014825
    DOI: 10.1142/S0129183109014825
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    Cited by:

    1. Zhu, Xuzhen & Tian, Hui & Zhang, Tianqiao, 2018. "Symmetrical information filtering via punishing superfluous diffusion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 1-9.

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