IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v494y2018icp403-409.html
   My bibliography  Save this article

Identifying online user reputation in terms of user preference

Author

Listed:
  • Dai, Lu
  • Guo, Qiang
  • Liu, Xiao-Lu
  • Liu, Jian-Guo
  • Zhang, Yi-Cheng

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.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:phsmap:v:494:y:2018:i:c:p:403-409
    DOI: 10.1016/j.physa.2017.12.020
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437117312669
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

    File URL: https://libkey.io/10.1016/j.physa.2017.12.020?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. Tao Jia & Dashun Wang & Boleslaw K. Szymanski, 2017. "Quantifying patterns of research-interest evolution," Nature Human Behaviour, Nature, vol. 1(4), pages 1-7, April.
    3. 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.
    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. Zhang, Yi-Lu & Guo, Qiang & Ni, Jing & Liu, Jian-Guo, 2015. "Memory effect of the online rating for movies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 417(C), pages 261-266.
    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. Xuan Liu & Meimei Chen & Jia Li & Ling Ma, 2019. "How to Manage Diversity and Enhance Team Performance: Evidence from Online Doctor Teams in China," IJERPH, MDPI, vol. 17(1), pages 1-17, December.

    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. 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.
    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. Li, Sheng-Nan & Guo, Qiang & Yang, Kai & Liu, Jian-Guo & Zhang, Yi-Cheng, 2018. "Uncovering the popularity mechanisms for Facebook applications," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 494(C), pages 422-429.
    4. Wang, Jia-Hua & Guo, Qiang & Yang, Kai & Zhang, Yi-Lu & Han, Jingti & Liu, Jian-Guo, 2016. "Popularity and user diversity of online objects," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 480-486.
    5. Wang, Ximeng & Liu, Yun & Xiong, Fei, 2016. "Improved personalized recommendation based on a similarity network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 271-280.
    6. Zhang, Yi-Lu & Guo, Qiang & Ni, Jing & Liu, Jian-Guo, 2015. "Memory effect of the online rating for movies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 417(C), pages 261-266.
    7. 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.
    8. 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.
    9. Yu, Xiaoyao & Szymanski, Boleslaw K. & Jia, Tao, 2021. "Become a better you: Correlation between the change of research direction and the change of scientific performance," Journal of Informetrics, Elsevier, vol. 15(3).
    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. Feng Shi & James Evans, 2023. "Surprising combinations of research contents and contexts are related to impact and emerge with scientific outsiders from distant disciplines," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    12. Li, Ren-De & Liu, Jian-Guo & Guo, Qiang & Zhang, Yi-Cheng, 2018. "Social signature identification of dynamical social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 213-222.
    13. Wenyuan Liu & Andrea Nanetti & Siew Ann Cheong, 2017. "Knowledge evolution in physics research: An analysis of bibliographic coupling networks," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-19, September.
    14. Cui, Haochuan & Zeng, An & Fan, Ying & Di, Zengru, 2021. "Quantifying the impact of a teamwork publication," Journal of Informetrics, Elsevier, vol. 15(4).
    15. Andrea Palmucci & Hao Liao & Andrea Napoletano & Andrea Zaccaria, 2020. "Where is your field going? A machine learning approach to study the relative motion of the domains of physics," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-16, June.
    16. van der Wouden, Frank & Youn, Hyejin, 2023. "The impact of geographical distance on learning through collaboration," Research Policy, Elsevier, vol. 52(2).
    17. Gao, Jian & Zhou, Tao, 2017. "Evaluating user reputation in online rating systems via an iterative group-based ranking method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 473(C), pages 546-560.
    18. Lu Liu & Benjamin F. Jones & Brian Uzzi & Dashun Wang, 2023. "Data, measurement and empirical methods in the science of science," Nature Human Behaviour, Nature, vol. 7(7), pages 1046-1058, July.
    19. Liang, Zhentao & Ba, Zhichao & Mao, Jin & Li, Gang, 2023. "Research complexity increases with scientists’ academic age: Evidence from library and information science," Journal of Informetrics, Elsevier, vol. 17(1).
    20. Dong, Ke & Wu, Jiang & Wang, Kaili, 2021. "On the inequality of citation counts of all publications of individual authors," Journal of Informetrics, Elsevier, vol. 15(4).

    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:eee:phsmap:v:494:y:2018:i:c:p:403-409. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.