Proceedings of the 10th international conference on Intelligent user interfaces
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
Yes, there is a correlation: - from social networks to personal behavior on the web
Proceedings of the 17th international conference on World Wide Web
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
What Have the Neighbours Ever Done for Us? A Collaborative Filtering Perspective
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
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
How far are we in trust-aware recommendation?
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Bayesian credibility modeling for personalized recommendation in participatory media
UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization
Resolving data sparsity and cold start in recommender systems
UMAP'12 Proceedings of the 20th international conference on User Modeling, Adaptation, and Personalization
Prior ratings: a new information source for recommender systems in e-commerce
Proceedings of the 7th ACM conference on Recommender systems
Proceedings of the 7th ACM conference on Recommender systems
A novel Bayesian similarity measure for recommender systems
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Merging trust in collaborative filtering to alleviate data sparsity and cold start
Knowledge-Based Systems
ACM Transactions on the Web (TWEB)
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Providing high quality recommendations is important for online systems to assist users who face a vast number of choices in making effective selection decisions. Collaborative filtering is a widely accepted technique to provide recommendations based on ratings of similar users. But it suffers from several issues like data sparsity and cold start. To address these issues, in this paper, we propose a simple but effective method, namely "Merge", to incorporate social trust information (i.e. trusted neighbors explicitly specified by users) in providing recommendations. More specifically, ratings of a user's trusted neighbors are merged to represent the preference of the user and to find similar other users for generating recommendations. Experimental results based on three real data sets demonstrate that our method is more effective than other approaches, both in accuracy and coverage of recommendations.