IEEE Internet Computing
Conceptual model of web service reputation
ACM SIGMOD Record
Quality driven web services composition
WWW '03 Proceedings of the 12th international conference on World Wide Web
The Eigentrust algorithm for reputation management in P2P networks
WWW '03 Proceedings of the 12th international conference on World Wide Web
Reputation and endorsement for web services
ACM SIGecom Exchanges - Chains of commitment
A model for web services discovery with QoS
ACM SIGecom Exchanges
PeerTrust: Supporting Reputation-Based Trust for Peer-to-Peer Electronic Communities
IEEE Transactions on Knowledge and Data Engineering
Reputation = f(User Ranking, Compliance, Verity)
ICWS '04 Proceedings of the IEEE International Conference on Web Services
Toward autonomic web services trust and selection
Proceedings of the 2nd international conference on Service oriented computing
An approach to modeling Web service QoS and provision price
WISEW'03 Proceedings of the Fourth international conference on Web information systems engineering workshops
Privacy-Aware Web Services in Smart Homes
ICOST '09 Proceedings of the 7th International Conference on Smart Homes and Health Telematics: Ambient Assistive Health and Wellness Management in the Heart of the City
A Service-Oriented Qos-Assured and Multi-Agent Cloud Computing Architecture
CloudCom '09 Proceedings of the 1st International Conference on Cloud Computing
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Web services selection is based on QoS and trust. As one of the important attributes of QoS, reputation is commonly used to assess the trustworthiness of the web services and minimize the threats of transactions. However, most existing reputation models of web services are all based on the subjective user ratings. These systems are easily attacked by malicious raters. This paper presents a novel reputation model named WSrep, in WSrep, the reputation integrates user ratings and a significant objective factor-credibility of QoS advertisements which is an objective view of the past behaviors of a given service. Other contributions of the paper include a customer measurable QoS model, a Bayesian learning model for building the credibility, and a set of experiments to show the benefits of our approach.