A converging reputation ranking iteration method via the eigenvector

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 reputatio...

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Bibliographic Details
Published in:PloS one 2022-10, Vol.17 (10), p.e0274567-e0274567
Main Authors: Liu, Xiao-Lu, Zhao, Chong
Format: Article
Language:English
Subjects:
Agglomeration
Algorithms
Analysis
Biology and Life Sciences
Convergence
Eigenvalues
Eigenvectors
Electronic commerce
Empirical analysis
Evaluation
Information management
Iterative algorithms
Physical Sciences
Ranking
Ratings & rankings
Reputations
Research and Analysis Methods
Social Sciences
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Summary: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 [tau] 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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0274567