Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior
Proceedings of the 2nd ACM conference on Electronic commerce
ARES '07 Proceedings of the The Second International Conference on Availability, Reliability and Security
Detecting bad-mouthing attacks on reputation systems using self-organizing maps
CISIS'11 Proceedings of the 4th international conference on Computational intelligence in security for information systems
Journal of Theoretical and Applied Electronic Commerce Research
A Dempster-Shafer theory based witness trustworthiness model
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
| Hi-index | 0.00 |
The problem of unfair testimonies remains an open issue in reputation systems for online trading communities. A common attempt is to use binary ratings to model sellers' reputation. However, this attempt leads to that the research of tackling unfair testimonies also focuses on reputation systems using binary ratings. In this extended abstract, we propose a two-stage clustering approach to filter unfair testimonies for reputation systems using multi-nominal ratings. The proposed approach uses clustering to identify unfair testimonies and further contributes to providing buyers a more accurate reputation evaluation regarding the target seller.