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
PeerTrust: Supporting Reputation-Based Trust for Peer-to-Peer Electronic Communities
IEEE Transactions on Knowledge and Data Engineering
Neural Network-Based Reputation Model in a Distributed System
CEC '04 Proceedings of the IEEE International Conference on E-Commerce Technology
IMM fuzzy probabilistic data association algorithm for tracking maneuvering target
Expert Systems with Applications: An International Journal
A two-stage methodology for gene regulatory network extraction from time-course gene expression data
Expert Systems with Applications: An International Journal
Decoupling service and feedback trust in a peer-to-peer reputation system
ISPA'05 Proceedings of the 2005 international conference on Parallel and Distributed Processing and Applications
An ontology and peer-to-peer based data and service unified discovery system
Expert Systems with Applications: An International Journal
Improving peer-to-peer search performance through intelligent social search
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
| Hi-index | 12.07 |
Peer-to-peer systems are open communities, in which not only is there no overarching control, but neither is there any hierarchy of control among the system components. In such open communities where peers can join and leave freely and behave autonomously, selecting appropriate peers to cooperate with is a challenging problem, since the candidate peers may be unreliable or dishonest. Reputation systems have been proposed to boost trust and enhance collaboration among peers. However, conventional computational reputation systems tend to generate trust based on ad hoc aggregation techniques thus produce reputation values with ambiguous meanings. In this paper we propose a probabilistic computational approach to model and generate reputation. By explicitly separating the reputation between providing services and giving recommendations, our solution represents the estimate of service quality for a specific transaction as a probability conditioned upon each retrieved recommendation, thus taking the innate behaviours of reporters into account. A Kalman filter is applied to learn further the service reputation from the estimate. The proposed approach works well even when there is sparse feedback from the reporting peers giving output with well-defined semantics and useful meanings.