The Eigentrust algorithm for reputation management in P2P networks
WWW '03 Proceedings of the 12th international conference on World Wide Web
PeerTrust: Supporting Reputation-Based Trust for Peer-to-Peer Electronic Communities
IEEE Transactions on Knowledge and Data Engineering
A survey of peer-to-peer content distribution technologies
ACM Computing Surveys (CSUR)
TRAVOS: Trust and Reputation in the Context of Inaccurate Information Sources
Autonomous Agents and Multi-Agent Systems
Trust management with delegation in grouped peer-to-peer communities
Proceedings of the eleventh ACM symposium on Access control models and technologies
A survey of trust and reputation systems for online service provision
Decision Support Systems
ARES '07 Proceedings of the The Second International Conference on Availability, Reliability and Security
Electronic Commerce Research and Applications
StereoTrust: a group based personalized trust model
Proceedings of the 18th ACM conference on Information and knowledge management
PROTECT: proximity-based trust-advisor using encounters for mobile societies
Proceedings of the 6th International Wireless Communications and Mobile Computing Conference
Bootstrapping trust evaluations through stereotypes
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Trust beyond reputation: A computational trust model based on stereotypes
Electronic Commerce Research and Applications
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Predicting trust among the agents is of great importance to various open distributed settings (e.g., emarket, peer-to-peer networks, etc.) in that dishonest agents can easily join the system and achieve their goals by circumventing agreed rules, or gaining unfair advantages, etc. Most existing trust mechanisms derive trust by statistically investigating the target agent's historical information. However, even if rich historical information is available, it is challenging to model an agent's behavior since an intelligent agent may strategically change its behavior to maximize its profits. We therefore propose a trust prediction approach to capture dynamic behavior of the target agent. Specifically, we first identify features which are capable of describing/ representing context of a transaction. Then we use these features to measure similarity between context of the potential transaction and that of previous transactions to estimate trustworthiness of the potential transaction based on previous similar transactions' outcomes. Evaluation using real auction data and synthetic data demonstrates efficacy of our approach in comparison with an existing representative trust mechanism.