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Improving the recommender algorithms with the detected communities in bipartite networks

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  • Zhang, Peng
  • Wang, Duo
  • Xiao, Jinghua

Abstract

Recommender system offers a powerful tool to make information overload problem well solved and thus gains wide concerns of scholars and engineers. A key challenge is how to make recommendations more accurate and personalized. We notice that community structures widely exist in many real networks, which could significantly affect the recommendation results. By incorporating the information of detected communities in the recommendation algorithms, an improved recommendation approach for the networks with communities is proposed. The approach is examined in both artificial and real networks, the results show that the improvement on accuracy and diversity can be 20% and 7%, respectively. This reveals that it is beneficial to classify the nodes based on the inherent properties in recommender systems.

Suggested Citation

  • Zhang, Peng & Wang, Duo & Xiao, Jinghua, 2017. "Improving the recommender algorithms with the detected communities in bipartite networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 147-153.
    • Handle: RePEc:eee:phsmap:v:471:y:2017:i:c:p:147-153
    DOI: 10.1016/j.physa.2016.11.076
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    References listed on IDEAS

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    1. Paul Resnick & Neophytos Iacovou & Mitesh Suchak & Peter Bergstrom & John Riedl, 1994. "GroupLens: An Open Architecture for Collaborative Filtering of Netnews," Working Paper Series 165, MIT Center for Coordination Science.
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    Cited by:

    1. Zare, Hadi & Nikooie Pour, Mina Abd & Moradi, Parham, 2019. "Enhanced recommender system using predictive network approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 520(C), pages 322-337.

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