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Information Filtering Via Clustering Coefficients Of User–Object Bipartite Networks

Author

Listed:
  • QIANG GUO

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

  • RUI LENG

    (Research Center of Complex Systems Science, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China)

  • KERUI SHI

    (Research Center of Complex Systems Science, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China)

  • JIAN-GUO LIU

    (Research Center of Complex Systems Science, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China)

Abstract

The clustering coefficient of user–object bipartite networks is presented to evaluate the overlap percentage of neighbors rating lists, which could be used to measure interest correlations among neighbor sets. Thecollaborative filtering(CF) information filtering algorithm evaluates a given user's interests in terms of his/her friends' opinions, which has become one of the most successful technologies for recommender systems. In this paper, different from the object clustering coefficient, users' clustering coefficients of user–object bipartite networks are introduced to improve the user similarity measurement. Numerical results for MovieLens and Netflix data sets show that users' clustering effects could enhance the algorithm performance. For MovieLens data set, the algorithmic accuracy, measured by the average ranking score, can be improved by 12.0% and the diversity could be improved by 18.2% and reach 0.649 when the recommendation list equals to 50. For Netflix data set, the accuracy could be improved by 14.5% at the optimal case and the popularity could be reduced by 13.4% comparing with the standard CF algorithm. Finally, we investigate the sparsity effect on the performance. This work indicates the user clustering coefficients is an effective factor to measure the user similarity, meanwhile statistical properties of user–object bipartite networks should be investigated to estimate users' tastes.

Suggested Citation

  • Qiang Guo & Rui Leng & Kerui Shi & Jian-Guo Liu, 2012. "Information Filtering Via Clustering Coefficients Of User–Object Bipartite Networks," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 23(02), pages 1-14.
  • Handle: RePEc:wsi:ijmpcx:v:23:y:2012:i:02:n:s012918311250012x
    DOI: 10.1142/S012918311250012X
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    Citations

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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.
    2. Zhang, Chu-Xu & Zhang, Zi-Ke & Yu, Lu & Liu, Chuang & Liu, Hao & Yan, Xiao-Yong, 2014. "Information filtering via collaborative user clustering modeling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 396(C), pages 195-203.

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