Proceedings of the 10th international conference on Intelligent user interfaces
IEEE Transactions on Knowledge and Data Engineering
Scalable collaborative filtering using cluster-based smoothing
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Computing and applying trust in web-based social networks
Computing and applying trust in web-based social networks
Trust-aware recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
Lessons from the Netflix prize challenge
ACM SIGKDD Explorations Newsletter - Special issue on visual analytics
Yes, there is a correlation: - from social networks to personal behavior on the web
Proceedings of the 17th international conference on World Wide Web
A wiki instance in the enterprise: opportunities, concerns and reality
Proceedings of the 2008 ACM conference on Computer supported cooperative work
Do you know?: recommending people to invite into your social network
Proceedings of the 14th international conference on Intelligent user interfaces
TrustWalker: a random walk model for combining trust-based and item-based recommendation
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
On social networks and collaborative recommendation
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Learning to recommend with social trust ensemble
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Formal trust model for multiagent systems
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Improving Prediction Accuracy in Trust-Aware Recommender Systems
HICSS '10 Proceedings of the 2010 43rd Hawaii International Conference on System Sciences
Use of social network information to enhance collaborative filtering performance
Expert Systems with Applications: An International Journal
Using trust in collaborative filtering recommendation
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
A matrix factorization technique with trust propagation for recommendation in social networks
Proceedings of the fourth ACM conference on Recommender systems
How far are we in trust-aware recommendation?
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
International Journal of Intelligent Systems
Bayesian credibility modeling for personalized recommendation in participatory media
UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization
The state-of-the-art in personalized recommender systems for social networking
Artificial Intelligence Review
Multimedia Tools and Applications
TFMAP: optimizing MAP for top-n context-aware recommendation
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
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
Inspectability and control in social recommenders
Proceedings of the sixth ACM conference on Recommender systems
Real-time top-n recommendation in social streams
Proceedings of the sixth ACM conference on Recommender systems
A trust-semantic fusion-based recommendation approach for e-business applications
Decision Support Systems
A novel Bayesian similarity measure for recommender systems
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Providing high quality recommendations is important for e-commerce systems to assist users in making effective selection decisions from a plethora of choices. Collaborative filtering is a widely accepted technique to generate recommendations based on the ratings of like-minded users. However, it suffers from several inherent issues such as data sparsity and cold start. To address these problems, we propose a novel method called ''Merge'' to incorporate social trust information (i.e., trusted neighbors explicitly specified by users) in providing recommendations. Specifically, ratings of a user's trusted neighbors are merged to complement and represent the preferences of the user and to find other users with similar preferences (i.e., similar users). In addition, the quality of merged ratings is measured by the confidence considering the number of ratings and the ratio of conflicts between positive and negative opinions. Further, the rating confidence is incorporated into the computation of user similarity. The prediction for a given item is generated by aggregating the ratings of similar users. Experimental results based on three real-world data sets demonstrate that our method outperforms other counterparts both in terms of accuracy and coverage.