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SibRank: Signed bipartite network analysis for neighbor-based collaborative ranking

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  • Shams, Bita
  • Haratizadeh, Saman

Abstract

Collaborative ranking is an emerging field of recommender systems that utilizes users’ preference data rather than rating values. Unfortunately, neighbor-based collaborative ranking has gained little attention despite its more flexibility and justifiability. This paper proposes a novel framework, called SibRank that seeks to improve the state of the art neighbor-based collaborative ranking methods. SibRank represents users’ preferences as a signed bipartite network, and finds similar users, through a novel personalized ranking algorithm in signed networks.

Suggested Citation

  • Shams, Bita & Haratizadeh, Saman, 2016. "SibRank: Signed bipartite network analysis for neighbor-based collaborative ranking," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 458(C), pages 364-377.
    • Handle: RePEc:eee:phsmap:v:458:y:2016:i:c:p:364-377
    DOI: 10.1016/j.physa.2016.04.025
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    References listed on IDEAS

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    1. Zhang, Zi-Ke & Zhou, Tao & Zhang, Yi-Cheng, 2010. "Personalized recommendation via integrated diffusion on user–item–tag tripartite graphs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(1), pages 179-186.
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    Cited by:

    1. Gu, Ke & Fan, Ying & Di, Zengru, 2020. "How to predict recommendation lists that users do not like," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).

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