RepRev: Mitigating the Negative Effects of Misreported Ratings

Yuan Liu, Siyuan Liu, Jie Zhang, Hui Fang, Han Yu, Chunyan Miao

Abstract: Reputation models depend on the ratings provided by buyers to gauge the reliability of sellers in multi-agent based e-commerce environment. However, there is no prevention for the 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 and utility loss recovered? In this work, we propose a mechanism to mitigate the negative effect of the misreported ratings. It temporarily inflates the reputation of the victim seller with a certain value for a period of time. This allows the seller to recover his utility loss due to lost opportunities caused by the misreported ratings. Experiments demonstrate the necessity and effectiveness of the proposed mechanism.