RepRev: Mitigating the Negative Effects of Misreported Ratings
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.
Cite
Text
Liu et al. "RepRev: Mitigating the Negative Effects of Misreported Ratings." AAAI Conference on Artificial Intelligence, 2014. doi:10.1609/AAAI.V28I1.9089Markdown
[Liu et al. "RepRev: Mitigating the Negative Effects of Misreported Ratings." AAAI Conference on Artificial Intelligence, 2014.](https://mlanthology.org/aaai/2014/liu2014aaai-reprev/) doi:10.1609/AAAI.V28I1.9089BibTeX
@inproceedings{liu2014aaai-reprev,
title = {{RepRev: Mitigating the Negative Effects of Misreported Ratings}},
author = {Liu, Yuan and Liu, Siyuan and Zhang, Jie and Fang, Hui and Yu, Han and Miao, Chunyan},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2014},
pages = {3124-3125},
doi = {10.1609/AAAI.V28I1.9089},
url = {https://mlanthology.org/aaai/2014/liu2014aaai-reprev/}
}