Iterative group-based and difference ranking method for online rating systems with spamming attacks

Quan-Yun Fu, Jian-Feng Ren and Hong-Liang Sun
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Quan-Yun Fu: School of Information Engineering, Nanjing University of Finance and Economics, Nanjing 210023, P. R. China
Jian-Feng Ren: School of Computer Science, University Nottingham Ningbo China. Ningbo, Hangzhou 315100, P. R. China
Hong-Liang Sun: School of Information Engineering, Nanjing University of Finance and Economics, Nanjing 210023, P. R. China

International Journal of Modern Physics C (IJMPC), 2021, vol. 32, issue 05, 1-14

Abstract: It is significant to assign reputation scores to users and identify spammers in the bipartite rating networks. In this paper, we propose an Iterative Group-based and Difference Ranking (IGDR) method, which is based on the original Iterative Group-based Ranking (IGR) method. The IGR method considers users grouping behaviors, but it ignores the characteristics of the individual ratings. It is discovered that individual rating characteristics could also contribute to the redistribution of reputation scores of users. The user with a smaller rating deviation will be given a higher reputation score. The proposed method outperforms IGR method ranging from 8% to 163% tested on three real datasets. It also can be applied to deal with big data in a short time.

Keywords: Complex networks; rating networks; spamming attacks (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1142/S0129183121500595

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