Title of Paper: : A converging reputation ranking iteration method via the eigenvector

Journal: : PLoS ONE

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.

Indexed by: : Journal paper

Discipline: : Management Science

First-Level Discipline: : Management Science and Engineering

Document Type: : J

Volume: : 17

Issue: : 10

Translation or Not: : no

Date of Publication: : 2022-10-01

Included Journals: : SCI