The elements of computer credibility
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
REGRET: reputation in gregarious societies
Proceedings of the fifth international conference on Autonomous agents
Judgement of information quality and cognitive authority in the Web
Journal of the American Society for Information Science and Technology
The Eigentrust algorithm for reputation management in P2P networks
WWW '03 Proceedings of the 12th international conference on World Wide Web
IEEE Transactions on Knowledge and Data Engineering
The relationship between Precision-Recall and ROC curves
ICML '06 Proceedings of the 23rd international conference on Machine learning
Social Information Processing in News Aggregation
IEEE Internet Computing
Discovery of Web user communities and their role in personalization
User Modeling and User-Adapted Interaction
A simple but effective method to incorporate trusted neighbors in recommender systems
UMAP'12 Proceedings of the 20th international conference on User Modeling, Adaptation, and Personalization
Merging trust in collaborative filtering to alleviate data sparsity and cold start
Knowledge-Based Systems
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In this paper, we focus on the challenge that users face in processing messages on the web posted in participatory media settings, such as blogs It is desirable to recommend to users a restricted set of messages that may be most valuable to them Credibility of a message is an important criteria to judge its value In our approach, theories developed in sociology, political science and information science are used to design a model for evaluating the credibility of messages that is user-specific and that is sensitive to the social network in which the user resides To recommend new messages to users, we employ Bayesian learning, built on past user behaviour, integrating new concepts of context and completeness of messages inspired from the strength of weak ties hypothesis, from social network theory We are able to demonstrate that our method is effective in providing the most credible messages to users and significantly enhances the performance of collaborative filtering recommendation, through a user study on the digg.com dataset.