An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior
Proceedings of the 2nd ACM conference on Electronic commerce
Communications of the ACM
Collaborative reputation mechanisms for electronic marketplaces
Decision Support Systems - Special issue for business to business electronic commerce, issues and solutions
Computing and using reputations for internet ratings
Proceedings of the 3rd ACM conference on Electronic Commerce
Extracting reputation in multi agent systems by means of social network topology
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
Reputation and social network analysis in multi-agent systems
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
A Social Mechanism of Reputation Management in Electronic Communities
CIA '00 Proceedings of the 4th International Workshop on Cooperative Information Agents IV, The Future of Information Agents in Cyberspace
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Enhancing reputation mechanisms via online social networks
EC '04 Proceedings of the 5th ACM conference on Electronic commerce
PeerTrust: Supporting Reputation-Based Trust for Peer-to-Peer Electronic Communities
IEEE Transactions on Knowledge and Data Engineering
A survey of trust and reputation systems for online service provision
Decision Support Systems
Multidimensional credibility model for neighbor selection in collaborative recommendation
Expert Systems with Applications: An International Journal
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Reputation systems are emerging as one of the promising solutions for building trust among market participants in e-commerce. Finding ways to avoid or reduce the influence of unfair ratings is a fundamental problem in reputation systems. We propose an implicit reputation rating mechanism suitable for B2C e-commerce. The conceptual framework of the mechanism is based on the source credibility model in consumer psychology. We have experimentally evaluated the performance of the mechanism by comparing with the other benchmark rating mechanisms. The experimental results provide evidence that the general users' opinions can be predicted more effectively by only a small number of users selected by our proposed mechanism.