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Improved Collaborative Filtering Algorithm Via Information Transformation

Author

Listed:
  • JIAN-GUO LIU

    (Department of Modern Physics and Nonlinear Science Center, University of Science and Technology of China, Hefei 230026, P. R. China;
    The Research Center for Complex Systems, University of Shanghai for Science and Technology, and Shanghai Academy of System Science, Shanghai 200093, P. R. China;
    Department of Physics, University of Fribourg, Chemin du Musée 3, CH-1700 Fribourg, Switzerland)

  • BING-HONG WANG

    (Department of Modern Physics and Nonlinear Science Center, University of Science and Technology of China, Hefei 230026, P. R. China;
    The Research Center for Complex Systems, University of Shanghai for Science and Technology, and Shanghai Academy of System Science, Shanghai 200093, P. R. China;
    Department of Physics, University of Fribourg, Chemin du Musée 3, CH-1700 Fribourg, Switzerland)

  • QIANG GUO

    (Dalian Nationalities University, Dalian 116600, P. R. China)

Abstract

In this paper, we propose a spreading activation approach for collaborative filtering (SA-CF). By using the opinion spreading process, the similarity between any users can be obtained. The algorithm has remarkably higher accuracy than the standard collaborative filtering using the Pearson correlation. Furthermore, we introduce a free parameter β to regulate the contributions of objects to user–user correlations. The numerical results indicate that decreasing the influence of popular objects can further improve the algorithmic accuracy and personality. We argue that a better algorithm should simultaneously require less computation and generate higher accuracy. Accordingly, we further propose an algorithm involving only the top-Nsimilar neighbors for each target user, which has both less computational complexity and higher algorithmic accuracy.

Suggested Citation

  • Jian-Guo Liu & Bing-Hong Wang & Qiang Guo, 2009. "Improved Collaborative Filtering Algorithm Via Information Transformation," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 20(02), pages 285-293.
    • Handle: RePEc:wsi:ijmpcx:v:20:y:2009:i:02:n:s0129183109013613
    DOI: 10.1142/S0129183109013613
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    Citations

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    Cited by:

    1. Liu, Ji & Deng, Guishi, 2009. "Link prediction in a user–object network based on time-weighted resource allocation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(17), pages 3643-3650.
    2. Yu, Fei & Zeng, An & Gillard, Sébastien & Medo, Matúš, 2016. "Network-based recommendation algorithms: A review," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 452(C), pages 192-208.
    3. Pan, Ying & Li, De-Hua & Liu, Jian-Guo & Liang, Jing-Zhang, 2010. "Detecting community structure in complex networks via node similarity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(14), pages 2849-2857.

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