Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior
Proceedings of the 2nd ACM conference on Electronic commerce
Detecting deception in reputation management
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
TRAVOS: Trust and Reputation in the Context of Inaccurate Information Sources
Autonomous Agents and Multi-Agent Systems
An Entropy-Based Approach to Protecting Rating Systems from Unfair Testimonies
IEICE - Transactions on Information and Systems
Electronic Commerce Research and Applications
Securing rating aggregation systems using statistical detectors and trust
IEEE Transactions on Information Forensics and Security - Special issue on electronic voting
iCLUB: an integrated clustering-based approach to improve the robustness of reputation systems
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Journal of Theoretical and Applied Electronic Commerce Research
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Reputation systems are highly prone to unfair rating attacks. Though many approaches for detecting unfair ratings have been proposed so far, their performance is often affected by the environment where they are applied. For a given unknown real environment, it is difficult to choose the most suitable approach for detecting unfair ratings as the ground truth data necessary to evaluate the accuracy of the detection approaches remains unknown. In this paper, we propose a novel Context-AwaRE (CARE) framework, to choose the most suitable unfair rating detection approach for a given unknown real environment. The framework first identifies simulated environments, closely similar to that of the unknown environment. The detection approaches performing well in the most similar simulated environments are then chosen as the suitable ones for the unknown real environment. Detailed experiments illustrate that the CARE framework can choose the most suitable detection approach to accurately distinguish fair and unfair ratings for any given unknown environment.