Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior
Proceedings of the 2nd ACM conference on Electronic commerce
Communications of the ACM
Explaining collaborative filtering recommendations
CSCW '00 Proceedings of the 2000 ACM conference on Computer supported cooperative work
An evidential model of distributed reputation management
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
Conceptual model of web service reputation
ACM SIGMOD Record
Personalized location-based brokering using an agent-based intermediary architecture
Decision Support Systems - Special issue: Agents and e-commerce business models
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
Detecting deception in reputation management
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Collaborative Reputation Mechanisms in Electronic Marketplaces
HICSS '99 Proceedings of the Thirty-second Annual Hawaii International Conference on System Sciences-Volume 8 - Volume 8
Reputation = f(User Ranking, Compliance, Verity)
ICWS '04 Proceedings of the IEEE International Conference on Web Services
Defining and Monitoring Service-Level Agreements for Dynamic e-Business
LISA '02 Proceedings of the 16th USENIX conference on System administration
Managing Trustworthiness in Component-based Embedded Systems
Electronic Notes in Theoretical Computer Science (ENTCS)
Improving Web Service Discovery with Usage Data
IEEE Software
Proceedings of the 9th annual ACM international workshop on Web information and data management
CAT: a context-aware trust model for open and dynamic systems
Proceedings of the 2008 ACM symposium on Applied computing
Association-based dynamic computation of reputation in web services
International Journal of Web and Grid Services
On the reputation of communities of web services
NOTERE '08 Proceedings of the 8th international conference on New technologies in distributed systems
ATM: an automatic trust monitoring algorithm for service software
Proceedings of the 2009 ACM symposium on Applied Computing
Mnikr: reputation construction through human trading of distributed social identities
Proceedings of the 5th ACM workshop on Digital identity management
Web service discovery based on past user experience
BIS'07 Proceedings of the 10th international conference on Business information systems
Trustworthy interaction balancing in mixed service-oriented systems
Proceedings of the 2010 ACM Symposium on Applied Computing
Towards semantic event-driven systems
NTMS'09 Proceedings of the 3rd international conference on New technologies, mobility and security
Modeling and mining of dynamic trust in complex service-oriented systems
Information Systems
Enabling reputation interoperability through semantic technologies
Proceedings of the 6th International Conference on Semantic Systems
Recommending multimedia web services in a multi-device environment
Information Systems
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Reputation systems are typically based on ratings given by the users. When there are no mechanisms in place to detect collusion and deception, combining user testimonies as such to form a provider's reputation may not give an accurate assessment, especially if the context of the ratings is not known. Moreover, such systems are vulnerable to manipulations by malicious users. Hence it becomes essential to establish the validity of the ratings prior to using them in formulating reputation based on such ratings. It is important to identify the rationale behind the ratings so that similar ratings (or ratings pertaining to a context) can be aggregated to obtain a reputation value meaningful in that context. We propose a fuzzy approach to analyze user rating behavior to infer the rationale for ratings in a web services environment. This inference of rationale facilitates the system to validate ratings, detect deception and collusion, identify user preferences and provide recommendations to users.