IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0097146.html
   My bibliography  Save this article

Ranking Reputation and Quality in Online Rating Systems

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0097146
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0097146&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0097146?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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).
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Song, Wen-Jun & Guo, Qiang & Liu, Jian-Guo, 2014. "Improved hybrid information filtering based on limited time window," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 192-197.
    2. Chen, Ling-Jiao & Zhang, Zi-Ke & Liu, Jin-Hu & Gao, Jian & Zhou, Tao, 2017. "A vertex similarity index for better personalized recommendation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 607-615.
    3. Dai, Lu & Guo, Qiang & Liu, Xiao-Lu & Liu, Jian-Guo & Zhang, Yi-Cheng, 2018. "Identifying online user reputation in terms of user preference," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 494(C), pages 403-409.
    4. 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.
    5. Shang, Junfeng & Wang, Yougui, 2013. "Rating the raters in a mixed model: An approach to deciphering the rater reliability," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(10), pages 2447-2459.
    6. 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.
    7. Mingxin Gan, 2014. "Walking on a User Similarity Network towards Personalized Recommendations," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-27, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0097146. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.