An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Supporting Trust in Virtual Communities
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 6 - Volume 6
Proceedings of the 10th international conference on Intelligent user interfaces
A trust-enhanced recommender system application: Moleskiing
Proceedings of the 2005 ACM symposium on Applied computing
The Structure and Dynamics of Networks: (Princeton Studies in Complexity)
The Structure and Dynamics of Networks: (Princeton Studies in Complexity)
Investigating interactions of trust and interest similarity
Decision Support Systems
Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness
ACM Transactions on Internet Technology (TOIT)
Trust-aware recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
Improving supervised learning performance by using fuzzy clustering method to select training data
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Fuzzy theory and technology with applications
Controversial users demand local trust metrics: an experimental study on Epinions.com community
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Improving Prediction Accuracy in Trust-Aware Recommender Systems
HICSS '10 Proceedings of the 2010 43rd Hawaii International Conference on System Sciences
Improved trust-aware recommender system using small-worldness of trust networks
Knowledge-Based Systems
Identifying mislabeled training data with the aid of unlabeled data
Applied Intelligence
Applied Intelligence
A dynamic trust model based on naive bayes classifier for ubiquitous environments
HPCC'06 Proceedings of the Second international conference on High Performance Computing and Communications
Generating predictive movie recommendations from trust in social networks
iTrust'06 Proceedings of the 4th international conference on Trust Management
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Trust -- aware recommender systems are intelligent technology applications that make use of trust information and user personal data in social networks to provide personalized recommendations. Recent research on recommender systems shows that these recommender systems are more robust against shilling attacks and can better be used for generating recommendations for new users. In this paper we proposed a model for improving the accuracy of trust-aware recommender systems. The results of evaluating our approach on Extended Epinions dataset shows that this approach can improve accuracy of recommender systems significantly while does not reduce the coverage of recommender systems.