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A converging reputation ranking iteration method via the eigenvector

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  • Xiao-Lu Liu
  • Chong Zhao

Abstract

Ranking user reputation and object quality in online rating systems is of great significance for the construction of reputation systems. In this paper we put forward an iterative algorithm for ranking reputation and quality in terms of eigenvector, named EigenRank algorithm, where the user reputation and object quality interact and the user reputation converges to the eigenvector associated to the greatest eigenvalue of a certain matrix. In addition, we prove the convergence of EigenRank algorithm, and analyse the speed of convergence. Meanwhile, the experimental results for the synthetic networks show that the AUC values and Kendall’s τ of the EigenRank algorithm are greater than the ones from the IBeta method and Vote Aggregation method with different proportions of random/malicious ratings. The results for the empirical networks show that the EigenRank algorithm performs better in accuracy and robustness compared to the IBeta method and Vote Aggregation method in the random and malicious rating attack cases. This work provides an expectable ranking algorithm for the online user reputation identification.

Suggested Citation

  • Xiao-Lu Liu & Chong Zhao, 2022. "A converging reputation ranking iteration method via the eigenvector," PLOS ONE, Public Library of Science, vol. 17(10), pages 1-13, October.
    • Handle: RePEc:plo:pone00:0274567
    DOI: 10.1371/journal.pone.0274567
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    References listed on IDEAS

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    1. Liu, Xiao-Lu & Guo, Qiang & Hou, Lei & Cheng, Can & Liu, Jian-Guo, 2015. "Ranking online quality and reputation via the user activity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 629-636.
    2. Liu, Xiao-Lu & Liu, Jian-Guo & Yang, Kai & Guo, Qiang & Han, Jing-Ti, 2017. "Identifying online user reputation of user–object bipartite networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 467(C), pages 508-516.
    3. Dai, Lu & Guo, Qiang & Liu, Xiao-Lu & Liu, Jian-Guo & Zhang, Yi-Cheng, 2018. "Identifying online user reputation in terms of user preference," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 494(C), pages 403-409.
    4. Gao, Jian & Zhou, Tao, 2017. "Evaluating user reputation in online rating systems via an iterative group-based ranking method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 473(C), pages 546-560.
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