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Ranking Reputation and Quality in Online Rating Systems

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  • Hao Liao
  • An Zeng
  • Rui Xiao
  • Zhuo-Ming Ren
  • Duan-Bing Chen
  • Yi-Cheng Zhang

Abstract

How to design an accurate and robust ranking algorithm is a fundamental problem with wide applications in many real systems. It is especially significant in online rating systems due to the existence of some spammers. In the literature, many well-performed iterative ranking methods have been proposed. These methods can effectively recognize the unreliable users and reduce their weight in judging the quality of objects, and finally lead to a more accurate evaluation of the online products. In this paper, we design an iterative ranking method with high performance in both accuracy and robustness. More specifically, a reputation redistribution process is introduced to enhance the influence of highly reputed users and two penalty factors enable the algorithm resistance to malicious behaviors. Validation of our method is performed in both artificial and real user-object bipartite networks.

Suggested Citation

  • 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.
    • Handle: RePEc:plo:pone00:0097146
    DOI: 10.1371/journal.pone.0097146
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    References listed on IDEAS

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    1. Yu, Yi-Kuo & Zhang, Yi-Cheng & Laureti, Paolo & Moret, Lionel, 2006. "Decoding information from noisy, redundant, and intentionally distorted sources," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 371(2), pages 732-744.
    2. Qian-Ming Zhang & An Zeng & Ming-Sheng Shang, 2013. "Extracting the Information Backbone in Online System," PLOS ONE, Public Library of Science, vol. 8(5), pages 1-7, May.
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    Cited by:

    1. Wei, Bo & Liu, Jie & Wei, Daijun & Gao, Cai & Deng, Yong, 2015. "Weighted k-shell decomposition for complex networks based on potential edge weights," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 420(C), pages 277-283.
    2. Leilei Wu & Zhuoming Ren & Xiao-Long Ren & Jianlin Zhang & Linyuan Lü, 2018. "Eliminating the Effect of Rating Bias on Reputation Systems," Complexity, Hindawi, vol. 2018, pages 1-11, February.
    3. Gao, Fujuan & Fenoaltea, Enrico Maria & Zhang, Yi-Cheng, 2023. "Market failure in a new model of platform design with partially informed consumers," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 619(C).
    4. Ikuesan Richard Adeyemi & Shukor Abd Razak & Mazleena Salleh & Hein S Venter, 2016. "Observing Consistency in Online Communication Patterns for User Re-Identification," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-27, December.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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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