Machine learning an artificial intelligence approach volume II
Machine learning an artificial intelligence approach volume II
Conceptual model of web service reputation
ACM SIGMOD Record
Building Effective Online Marketplaces with Institution-Based Trust
Information Systems Research
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
ARES '07 Proceedings of the The Second International Conference on Availability, Reliability and Security
A survey of trust in computer science and the Semantic Web
Web Semantics: Science, Services and Agents on the World Wide Web
A Review on Trust and Reputation for Web Service Selection
ICDCSW '07 Proceedings of the 27th International Conference on Distributed Computing Systems Workshops
A Research Agenda for Trust in Online Environments
Journal of Management Information Systems
A survey of attack and defense techniques for reputation systems
ACM Computing Surveys (CSUR)
Automated trust negotiation using cryptographic credentials
ACM Transactions on Information and System Security (TISSEC)
A Trust and Reputation Model Based on Bayesian Network for Web Services
ICWS '10 Proceedings of the 2010 IEEE International Conference on Web Services
Developing trust networks based on user tagging information for recommendation making
WISE'10 Proceedings of the 11th international conference on Web information systems engineering
Trust negotiation for semantic web services
SWSWPC'04 Proceedings of the First international conference on Semantic Web Services and Web Process Composition
eTrust: understanding trust evolution in an online world
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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Trust and reputation systems are widely adopted in the e-commerce environment to help buyers choose trustworthy sellers. It is a normal thought that the higher the reputation is, the more trustworthy its holder should be. However, our research discloses that under certain circumstances, a high-reputation seller has greater intention to cheat, which means that buyers should trust the low-reputation sellers better in those cases. We term this phenomenon TrustReputation Paradox. The theoretical proof, based on the game theory, is conducted to show the existence of the paradox. The root causes of this abnormality are revealed and discussed. In the end, we provide some guidelines for trust and reputation system designers to avoid this obscure pitfall.