Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior
Proceedings of the 2nd 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
A logic for uncertain probabilities
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
AAMAS '02 Revised Papers from the Workshop on Agent Mediated Electronic Commerce on Agent-Mediated Electronic Commerce IV, Designing Mechanisms and Systems
Mining knowledge-sharing sites for viral marketing
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
The Eigentrust algorithm for reputation management in P2P networks
WWW '03 Proceedings of the 12th international conference on World Wide Web
A Computational Model of Trust and Reputation for E-businesses
HICSS '02 Proceedings of the 35th Annual Hawaii International Conference on System Sciences (HICSS'02)-Volume 7 - Volume 7
Detecting deception in reputation management
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Collaborative Reputation Mechanisms in Electronic Marketplaces
HICSS '99 Proceedings of the Thirty-second Annual Hawaii International Conference on System Sciences-Volume 8 - Volume 8
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
CONFESS " An Incentive Compatible Reputation Mechanism for the Online Hotel Booking Industry
CEC '04 Proceedings of the IEEE International Conference on E-Commerce Technology
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
Monopolizing markets by exploiting trust
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
An incentives' mechanism promoting truthful feedback in peer-to-peer systems
CCGRID '05 Proceedings of the Fifth IEEE International Symposium on Cluster Computing and the Grid - Volume 01
A survey of trust and reputation systems for online service provision
Decision Support Systems
Eliciting Informative Feedback: The Peer-Prediction Method
Management Science
Collusion-resistant, incentive-compatible feedback payments
Proceedings of the 8th ACM conference on Electronic commerce
Eliciting bid taker non-price preferences in (combinatorial) auctions
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Formal trust model for multiagent systems
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
CRM: An efficient trust and reputation model for agent computing
Knowledge-Based Systems
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In the context of electronic commerce, when modeling the trustworthiness of selling agent relies (in part) on propagating ratings provided by buying agents that have personal experience with the seller, the problem of unfair ratings arises. Extreme diversity of open and dynamic electronic marketplaces causes difficulties in handling unfair ratings in trust management systems. To ease this problem, we propose a novel trust-based incentive mechanism for eliciting fair ratings of sellers from buyers. In our mechanism, buyers model other buyers, using an approach that combines both private and public reputation values. In addition, however, sellers model the reputation of buyers. Reputable buyers provide fair ratings of sellers, and are likely considered trustworthy by many other buyers. In marketplaces operating with our mechanism, sellers will offer more attractive products to satisfy reputable buyers, in order to build their reputation. In consequence, our mechanism creates incentives for buyers to provide fair ratings of sellers, leading to more effective e-marketplaces where honest buyers and sellers can gain more profit.