Communications of the ACM
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
Proceedings of the 10th international conference on Intelligent user interfaces
IEEE Transactions on Knowledge and Data Engineering
Trust-aware recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
Trust-based recommendation systems: an axiomatic approach
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
On social networks and collaborative recommendation
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Social networks and interest similarity: the case of CiteULike
Proceedings of the 21st ACM conference on Hypertext and hypermedia
Putting things in context: Challenge on Context-Aware Movie Recommendation
Proceedings of the Workshop on Context-Aware Movie Recommendation
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Putting things in context: Challenge on Context-Aware Movie Recommendation
Proceedings of the Workshop on Context-Aware Movie Recommendation
Introduction to special section on CAMRa2010: Movie recommendation in context
ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on twitter and microblogging services, social recommender systems, and CAMRa2010: Movie recommendation in context
A flexible framework for context-aware recommendations in the social commerce domain
Proceedings of the Joint EDBT/ICDT 2013 Workshops
Advances in Clustering Collaborative Filtering by means of Fuzzy C-means and trust
Expert Systems with Applications: An International Journal
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The sparse nature of historical rating profile hinders reliable similarity metrics between users, leading to poor recommendation performance. The availability of user social networks and user opinions can be incorporated to improve prediction accuracy. One of the key points is how to make the multiple sources of information consistent for the purpose of recommendation. In this paper, we proposed Local Trust Network (LTN) based recommendation method in the setting of movie recommendation, that mines the social network and multiple sources of user opinions to generate a highly reliable trust user network, upon which a recommendation is made. With transductive reasoning, LTN interpret trust user as a collection of instances, so it is well suited for the sparse issue of social network information. Our experiments on CAMRa10 data set shows the proposed methods improve recommendation performance significantly.