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Degree Correlation Of Bipartite Network 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

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

Abstract

In this paper, the statistical property, namely degree correlation between users and objects, is taken into account and be embedded into the similarity index of collaborative filtering (CF) algorithm to improve the algorithmic performance. The numerical simulation on a benchmark data set shows that the algorithmic accuracy of the presented algorithm, measured by the average ranking score, is improved by 18.19% in the optimal case. The statistical analysis on the product distribution of the user and object degrees indicate that, in the optimal case, the distribution obeys the power-law and the exponential is equal to -2.33. Numerical results show that the presented algorithm can provide more diverse and less popular recommendations, for example, when the recommendation list contains 10 objects, the diversity, measured by the hamming distance, is improved by 21.90%. Since all of the real recommendation data evolving with time, this work may shed some light on the adaptive recommendation algorithm which could change its parameter automatically according to the statistical properties of the user-object bipartite network.

Suggested Citation

  • Jian-Guo Liu & Tao Zhou & Bing-Hong Wang & Yi-Cheng Zhang & Qiang Guo, 2010. "Degree Correlation Of Bipartite Network On Personalized Recommendation," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 21(01), pages 137-147.
    • Handle: RePEc:wsi:ijmpcx:v:21:y:2010:i:01:n:s0129183110014999
    DOI: 10.1142/S0129183110014999
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    Cited by:

    1. Jiang, Liang-Chao & Liu, Run-Ran & Jia, Chun-Xiao, 2022. "User-location distribution serves as a useful feature in item-based collaborative filtering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).

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