Communications of the ACM
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
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
Propagation of trust and distrust
Proceedings of the 13th international conference on World Wide Web
Review on Computational Trust and Reputation Models
Artificial Intelligence Review
A survey of trust and reputation systems for online service provision
Decision Support Systems
A typology of complaints about eBay sellers
Communications of the ACM - The psychology of security: why do good users make bad decisions?
Reducing internet auction fraud
Communications of the ACM - Web searching in a multilingual world
Analyzing seller practices in a Brazilian marketplace
Proceedings of the 18th international conference on World wide web
The effects of source credibility ratings in a cultural heritage information aggregator
Proceedings of the 3rd workshop on Information credibility on the web
Blog credibility ranking by exploiting verified content
Proceedings of the 3rd workshop on Information credibility on the web
Cutting-plane training of structural SVMs
Machine Learning
Fraud detection in reputation systems in e-markets using logistic regression
Proceedings of the 2010 ACM Symposium on Applied Computing
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The popularization of Web has given rise to new services every day, demanding mechanisms to ensure the credibility of these services. Since now, little has been done to measure and understand the credibility of this complex Web environment, which itself is a major research challenge. From the challenges related to the task of assigning a credibility value to an online service in Web 2.0 applications, we propose a framework for the design, implementation and evaluation of credibility models. We call a credibility model a function capable of assigning a credibility value to a transaction of a Web application, considering different criteria of this service and its supplier. To validate this framework and models, we perform experiments using an actual dataset, from which we evaluated different credibility models using distinct types of information sources, and it allows to compare and evaluate these credibility models. The obtained results are very good, showing representative gains, when compared to a baseline and also with a known state-of-the-art approach. The results confirm that the credibility framework can be used to enforce trust to users of services on the Web.