Identifying online user reputation in terms of user preference

Lu Dai, Qiang Guo, Xiao-Lu Liu, Jian-Guo Liu and Yi-Cheng Zhang

Physica A: Statistical Mechanics and its Applications, 2018, vol. 494, issue C, 403-409

Abstract: Identifying online user reputation is significant for online social systems. In this paper, taking into account the preference physics of online user collective behaviors, we present an improved group-based rating method for ranking online user reputation based on the user preference (PGR). All the ratings given by each specific user are mapped to the same rating criteria. By grouping users according to their mapped ratings, the online user reputation is calculated based on the corresponding group sizes. Results for MovieLens and Netflix data sets show that the AUC values of the PGR method can reach 0.9842 (0.9493) and 0.9995 (0.9987) for malicious (random) spammers, respectively, outperforming the results generated by the traditional group-based method, which indicates that the online preference plays an important role for measuring user reputation.

Keywords: User-object bipartite networks; Reputation evaluation; Ranking method; User preference (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:494:y:2018:i:c:p:403-409

DOI: 10.1016/j.physa.2017.12.020

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