Identifying online user reputation of user–object bipartite networks

Liu, Xiao-Lu; Liu, Jian-Guo; Yang, Kai; Guo, Qiang; Han, Jing-Ti

doi:10.1016/j.physa.2016.10.031

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Journal article

Identifying online user reputation of user–object bipartite networks

Liu, Xiao-Lu Research Center of Complex Systems Science, University of Shanghai for Science and Technology, China
Liu, Jian-Guo Research Center of Complex Systems Science, University of Shanghai for Science and Technology, China - Data Science and Cloud Service Centre, Shanghai University of Finance and Economics, China - Department of Physics, University of Fribourg, Switzerland
Yang, Kai Research Center of Complex Systems Science, University of Shanghai for Science and Technology, China
Guo, Qiang Research Center of Complex Systems Science, University of Shanghai for Science and Technology, China
Han, Jing-Ti Data Science and Cloud Service Centre, Shanghai University of Finance and Economics, China

01.02.2017

Published in:

Physica A: Statistical Mechanics and its Applications. - 2017, vol. 467, p. 508–516

English Identifying online user reputation based on the rating information of the user–object bipartite networks is important for understanding online user collective behaviors. Based on the Bayesian analysis, we present a parameter-free algorithm for ranking online user reputation, where the user reputation is calculated based on the probability that their ratings are consistent with the main part of all user opinions. The experimental results show that the AUC values of the presented algorithm could reach 0.8929 and 0.8483 for the MovieLens and Netflix data sets, respectively, which is better than the results generated by the CR and IARR methods. Furthermore, the experimental results for different user groups indicate that the presented algorithm outperforms the iterative ranking methods in both ranking accuracy and computation complexity. Moreover, the results for the synthetic networks show that the computation complexity of the presented algorithm is a linear function of the network size, which suggests that the presented algorithm is very effective and efficient for the large scale dynamic online systems.

Faculty

Faculté des sciences et de médecine

Department

Département de Physique

Language