An evidential model of distributed reputation management
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
The Eigentrust algorithm for reputation management in P2P networks
WWW '03 Proceedings of the 12th international conference on World Wide Web
Supporting Trust in Virtual Communities
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 6 - Volume 6
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
Trust Metrics, Models and Protocols for Electronic Commerce Transactions
ICDCS '98 Proceedings of the The 18th International Conference on Distributed Computing Systems
Probabilistic Memory-Based Collaborative Filtering
IEEE Transactions on Knowledge and Data Engineering
Spreading Activation Models for Trust Propagation
EEE '04 Proceedings of the 2004 IEEE International Conference on e-Technology, e-Commerce and e-Service (EEE'04)
PeerTrust: Supporting Reputation-Based Trust for Peer-to-Peer Electronic Communities
IEEE Transactions on Knowledge and Data Engineering
Semantic constraints for trust transitivity
APCCM '05 Proceedings of the 2nd Asia-Pacific conference on Conceptual modelling - Volume 43
Trusted P2P Transactions with Fuzzy Reputation Aggregation
IEEE Internet Computing
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
Using Trust for Secure Collaboration in Uncertain Environments
IEEE Pervasive Computing
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Reputation System provides a way to maintain trust through social control by utilizing feedbacks about the service providers' past behaviors. Conventional Memory-based Reputation System (MRS) is one of the most successful mechanisms in terms of accuracy. Though MRS performs well on giving predicted values for service providers offering averaging quality services, our experiments show that MRS performs poor on giving predicted values for service providers offering high and low quality services. We propose a Bayesian Memory-based Reputation System (BMRS) which uses Bayesian Theory to analyze the probability distribution of the predicted valued given by MRS and makes suitable adjustment. The simulation results, which are based on EachMovie dataset, show that our proposed BMRS has higher accuracy than MRS on giving predicted values for service providers offering high and low quality services.