RepRev: Mitigating the Negative Effects of Misreported Ratings

Authors

  • Yuan Liu Nanyang Technological University
  • Siyuan Liu Nanyang Technological University
  • Jie Zhang Nanyang Technological University
  • Hui Fang Nanyang Technological University
  • Han Yu Nanyang Technological University
  • Chunyan Miao Nanyang Technological University

DOI:

https://doi.org/10.1609/aaai.v28i1.9089

Abstract

Reputation models depend on the ratings provided by buyers togauge the reliability of sellers in multi-agent based e-commerce environment. However, there is no prevention forthe cases in which a buyer misjudges a seller, and provides a negative rating to an original satisfactory transaction. In this case,how should the seller get his reputation repaired andutility loss recovered? In this work, we propose a mechanism to mitigate the negativeeffect of the misreported ratings. It temporarily inflates the reputation of thevictim seller with a certain value for a period of time. This allows the seller to recover hisutility loss due to lost opportunities caused by the misreported ratings. Experiments demonstrate the necessity and effectiveness of the proposed mechanism.

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Published

2014-06-21

How to Cite

Liu, Y., Liu, S., Zhang, J., Fang, H., Yu, H., & Miao, C. (2014). RepRev: Mitigating the Negative Effects of Misreported Ratings. Proceedings of the AAAI Conference on Artificial Intelligence, 28(1). https://doi.org/10.1609/aaai.v28i1.9089