A course in fuzzy systems and control
A course in fuzzy systems and control
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
Design and Analysis of Experiments
Design and Analysis of Experiments
A survey of trust and reputation systems for online service provision
Decision Support Systems
Bootstrapping trust evaluations through stereotypes
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Credibility: How Agents Can Handle Unfair Third-Party Testimonies in Computational Trust Models
IEEE Transactions on Knowledge and Data Engineering
Anomaly Detection in Feedback-based Reputation Systems through Temporal and Correlation Analysis
SOCIALCOM '10 Proceedings of the 2010 IEEE Second International Conference on Social Computing
Multi-layer cognitive filtering by behavioral modeling
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Proceedings of the 14th Annual International Conference on Electronic Commerce
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Reputation systems have become more and more important in facilitating transactions in online systems. However, the accuracy of reputation systems has always been a concern for the users due to the existence of unfair ratings. Though many approaches have been proposed to mitigate the adverse effect of unfair ratings, most of them use the credibility of the rating provider alone to decide whether the rating is unfair without considering other aspects of the rating itself. Models that do consider multiple aspects often combine them through arbitrarily set weights. Therefore, they cannot work well when the credibility is not evaluated accurately or when the weights are not set properly. To resolve this problem, in this paper, we propose a reputation model which considers and combines the temporal, similarity and quantity aspects of the user ratings based on fuzzy logic to improve the accuracy of reputation evaluation. Experimental results based on a set of real user data from a cyber competition show that the proposed model is more robust against unfair ratings than the existing approaches, especially under Sybil attack conditions.