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Evaluating user reputation in online rating systems via an iterative group-based ranking method

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  • Gao, Jian
  • Zhou, Tao

Abstract

Reputation is a valuable asset in online social lives and it has drawn increased attention. Due to the existence of noisy ratings and spamming attacks, how to evaluate user reputation in online rating systems is especially significant. However, most of the previous ranking-based methods either follow a debatable assumption or have unsatisfied robustness. In this paper, we propose an iterative group-based ranking method by introducing an iterative reputation–allocation process into the original group-based ranking method. More specifically, the reputation of users is calculated based on the weighted sizes of the user rating groups after grouping all users by their rating similarities, and the high reputation users’ ratings have larger weights in dominating the corresponding user rating groups. The reputation of users and the user rating group sizes are iteratively updated until they become stable. Results on two real data sets with artificial spammers suggest that the proposed method has better performance than the state-of-the-art methods and its robustness is considerably improved comparing with the original group-based ranking method. Our work highlights the positive role of considering users’ grouping behaviors towards a better online user reputation evaluation.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:phsmap:v:473:y:2017:i:c:p:546-560
    DOI: 10.1016/j.physa.2017.01.055
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    References listed on IDEAS

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    Citations

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    Cited by:

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    6. Jian Gao & Tao Zhou, 2017. "Quantifying China's Regional Economic Complexity," Papers 1703.01292, arXiv.org, revised Nov 2017.
    7. Yang, Xiao & Gao, Jian & Liu, Jin-Hu & Zhou, Tao, 2018. "Height conditions salary expectations: Evidence from large-scale data in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 501(C), pages 86-97.

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