Communications of the ACM
Computing and using reputations for internet ratings
Proceedings of the 3rd ACM conference on Electronic Commerce
Notions of reputation in multi-agents systems: a review
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
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
Coping with inaccurate reputation sources: experimental analysis of a probabilistic trust model
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
A survey of trust and reputation systems for online service provision
Decision Support Systems
Electronic Commerce Research and Applications
Learning to trust in the competence and commitment of agents
Autonomous Agents and Multi-Agent Systems
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
Automatically assessing review helpfulness
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
A probabilistic model for trust and reputation
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
A probabilistic approach for maintaining trust based on evidence
Journal of Artificial Intelligence Research
Multi-layer cognitive filtering by behavioral modeling
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
SmartRate: a rating interpretation mechanism for agents in smart grid markets
Proceedings of the 14th Annual International Conference on Electronic Commerce
An empirical evaluation of geometric subjective logic operators
AT'13 Proceedings of the Second international conference on Agreement Technologies
| Hi-index | 0.00 |
Several reputation models have been introduced to deal with the problem of biased reputation providers. Most of these models discount or discard biased information received from the reputation providers, and most of them are not appropriate when a large population of information providers are biased or dishonest. In this paper, we present a probabilistic approach for reputation modeling, the Probabilistic Reputation model (PRep). PRep models a reputation provider's behavior, and uses this model to re-interpret the reported information, thus making use of the entire reputation reports effectively, even if they are biased. The re-interpreted data is combined with the agent's direct experiences to determine an overall level of trust in the third-party agent. We show that PRep significantly outperforms two state-of-the-art trust and reputation models---HAPTIC and TRAVOS---and improves the overall payoff in a game-theoretic environment.