ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research and Development
Detecting deception in reputation management
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Review on Computational Trust and Reputation Models
Artificial Intelligence Review
Explanation in Case-Based Reasoning---Perspectives and Goals
Artificial Intelligence Review
TRAVOS: Trust and Reputation in the Context of Inaccurate Information Sources
Autonomous Agents and Multi-Agent Systems
A survey of trust and reputation systems for online service provision
Decision Support Systems
Dynamically learning sources of trust information: experience vs. reputation
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
RepTrap: a novel attack on feedback-based reputation systems
Proceedings of the 4th international conference on Security and privacy in communication netowrks
A personalized framework for trust assessment
Proceedings of the 2009 ACM symposium on Applied Computing
Operators for propagating trust and their evaluation in social networks
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Bayesian reputation modeling in E-marketplaces sensitive to subjecthity, deception and change
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Evidence-based trust: A mathematical model geared for multiagent systems
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
Multi-layer cognitive filtering by behavioral modeling
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
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
Hi-index | 0.00 |
Many trust models have been proposed to evaluate seller trustworthiness in multiagent e-marketplaces. Their performance varies highly depending on environments where they are applied. However, it is challenging to choose suitable models for environments where ground truth about seller trustworthiness is unknown (called unknown environments). We propose a novel framework to choose suitable trust models for unknown environments, based on the intuition that if a model performs well in one environment, it will do so in another similar environment. Specifically, for an unknown environment, we identify a similar simulated environment (with known ground truth) where the trust model performing the best will be chosen as the suitable solution. Evaluation results confirm the effectiveness of our framework in choosing suitable trust models for different environments.