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Personal recommendation via modified collaborative filtering

Author

Listed:
  • Liu, Run-Ran
  • Jia, Chun-Xiao
  • Zhou, Tao
  • Sun, Duo
  • Wang, Bing-Hong

Abstract

In this paper, we propose a novel method to compute the similarity between congeneric nodes in bipartite networks. Different from the standard cosine similarity, we take into account the influence of a node’s degree. Substituting this new definition of similarity for the standard cosine similarity, we propose a modified collaborative filtering (MCF). Based on a benchmark database, we demonstrate the great improvement of algorithmic accuracy for both user-based MCF and object-based MCF.

Suggested Citation

  • Liu, Run-Ran & Jia, Chun-Xiao & Zhou, Tao & Sun, Duo & Wang, Bing-Hong, 2009. "Personal recommendation via modified collaborative filtering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(4), pages 462-468.
    • Handle: RePEc:eee:phsmap:v:388:y:2009:i:4:p:462-468
    DOI: 10.1016/j.physa.2008.10.010
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    Citations

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    Cited by:

    1. Chen, Guilin & Gao, Tianrun & Zhu, Xuzhen & Tian, Hui & Yang, Zhao, 2017. "Personalized recommendation based on preferential bidirectional mass diffusion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 397-404.
    2. 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).
    3. Chen, Ling-Jiao & Zhang, Zi-Ke & Liu, Jin-Hu & Gao, Jian & Zhou, Tao, 2017. "A vertex similarity index for better personalized recommendation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 607-615.
    4. Ramezani, Mohsen & Moradi, Parham & Akhlaghian, Fardin, 2014. "A pattern mining approach to enhance the accuracy of collaborative filtering in sparse data domains," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 408(C), pages 72-84.
    5. Chen, Ling-Jiao & Gao, Jian, 2018. "A trust-based recommendation method using network diffusion processes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 679-691.
    6. Sundaresan Bhaskaran & Raja Marappan & Balachandran Santhi, 2021. "Design and Analysis of a Cluster-Based Intelligent Hybrid Recommendation System for E-Learning Applications," Mathematics, MDPI, vol. 9(2), pages 1-21, January.

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