GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Communications of the ACM
PHOAKS: a system for sharing recommendations
Communications of the ACM
A Multilinear Singular Value Decomposition
SIAM Journal on Matrix Analysis and Applications
Methods and metrics for cold-start recommendations
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Ontological user profiling in recommender systems
ACM Transactions on Information Systems (TOIS)
An Accurate and Scalable Collaborative Recommender
Artificial Intelligence Review
CubeSVD: a novel approach to personalized Web search
WWW '05 Proceedings of the 14th international conference on World Wide Web
Tag recommendations based on tensor dimensionality reduction
Proceedings of the 2008 ACM conference on Recommender systems
Tag recommendations in social bookmarking systems
AI Communications
A comparison of content-based tag recommendations in folksonomy systems
KONT'07/KPP'07 Proceedings of the First international conference on Knowledge processing and data analysis
PointBurst: towards a trust-relationship framework for improved social recommendations
APWeb'12 Proceedings of the 14th international conference on Web Technologies and Applications
Tag-aware recommender systems: a state-of-the-art survey
Journal of Computer Science and Technology - Special issue on Community Analysis and Information Recommendation
An adaptive method for the tag-rating-based recommender system
AMT'12 Proceedings of the 8th international conference on Active Media Technology
ACM Transactions on Interactive Intelligent Systems (TiiS)
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Personalized recommendation is used to conquer the information overload problem, and collaborative filtering recommendation (CF) is one of the most successful recommendation techniques to date. However, CF becomes less effective when users have multiple interests, because users have similar taste in one aspect may behave quite different in other aspects. Information got from social bookmarking websites not only tells what a user likes, but also why he or she likes it. This paper proposes a division algorithm and a CubeSVD algorithm to analysis this information, distill the interrelations between different users’ various interests, and make better personalized recommendation based on them. Experiment reveals the superiority of our method over traditional CF methods.