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发表刊物：PLoS ONE

摘要：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.

论文类型：期刊论文

学科门类：管理学

一级学科：管理科学与工程

文献类型：J

卷号：17

期号：10

是否译文：否

发表时间：2022-10-01

收录刊物：SCI