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Identifying online user reputation of user–object bipartite networks

Author

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

Abstract

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.

Suggested Citation

  • Liu, Xiao-Lu & Liu, Jian-Guo & Yang, Kai & Guo, Qiang & Han, Jing-Ti, 2017. "Identifying online user reputation of user–object bipartite networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 467(C), pages 508-516.
    • Handle: RePEc:eee:phsmap:v:467:y:2017:i:c:p:508-516
    DOI: 10.1016/j.physa.2016.10.031
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    References listed on IDEAS

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    1. Ni, Jing & Zhang, Yi-Lu & Hu, Zhao-Long & Song, Wen-Jun & Hou, Lei & Guo, Qiang & Liu, Jian-Guo, 2014. "Ceiling effect of online user interests for the movies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 402(C), pages 134-140.
    2. Liu, Xiao-Lu & Guo, Qiang & Hou, Lei & Cheng, Can & Liu, Jian-Guo, 2015. "Ranking online quality and reputation via the user activity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 629-636.
    3. Shi, Gui-Yuan & Kong, Yi-Xiu & Liao, Hao & Zhang, Yi-Cheng, 2016. "Analysis of ground state in random bipartite matching," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 397-402.
    4. Liao, Hao & Zeng, An & Zhang, Yi-Cheng, 2015. "Predicting missing links via correlation between nodes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 216-223.
    5. Hao Liao & An Zeng & Rui Xiao & Zhuo-Ming Ren & Duan-Bing Chen & Yi-Cheng Zhang, 2014. "Ranking Reputation and Quality in Online Rating Systems," PLOS ONE, Public Library of Science, vol. 9(5), pages 1-7, May.
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    7. Guan-Nan Wang & Hui Gao & Lian Chen & Dennis N A Mensah & Yan Fu, 2015. "Predicting Positive and Negative Relationships in Large Social Networks," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-14, June.
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    Cited by:

    1. Chandra, Anita & Garg, Himanshu & Maiti, Abyayananda, 2019. "A general growth model for online emerging user–object bipartite networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 517(C), pages 370-384.
    2. Gu, Ke & Fan, Ying & Zeng, An & Zhou, Jianlin & Di, Zengru, 2018. "Analysis on large-scale rating systems based on the signed network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 507(C), pages 99-109.
    3. Chen, Ling-Jiao & Gao, Jian, 2018. "A trust-based recommendation method using network diffusion processes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 679-691.
    4. Wu, Ying-Ying & Guo, Qiang & Liu, Jian-Guo & Zhang, Yi-Cheng, 2018. "Effect of the initial configuration for user–object reputation systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 288-294.

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