Recommending and evaluating choices in a virtual community of use
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
A Scalable Collaborative Filtering Framework Based on Co-Clustering
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Orthogonal nonnegative matrix t-factorizations for clustering
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A Generalized Maximum Entropy Approach to Bregman Co-clustering and Matrix Approximation
The Journal of Machine Learning Research
Collaborative filtering using orthogonal nonnegative matrix tri-factorization
Information Processing and Management: an International Journal
Can movies and books collaborate?: cross-domain collaborative filtering for sparsity reduction
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Collaborative Filtering with CCAM
ICMLA '11 Proceedings of the 2011 10th International Conference on Machine Learning and Applications and Workshops - Volume 02
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Recommender systems have become an important research area because of a high interest from academia and industries. As a branch of recommender systems, collaborative filtering (CF) systems take its roots from sharing opinions with others and have been shown to be very effective for generating high quality recommendations. However, CF often confronts the sparsity problem, caused by fewer ratings against the unknowns that need to be predicted. In this paper, we consider a hybrid approach that combines content-based approach with collaborative filtering under a unified model called co-clustering with augmented matrices (CCAM). CCAM is based on information-theoretic co-clustering but further considers augmented data matrices like user profile and item description. By presenting results with a reduced error of prediction, we show that content-based information can help reduce the sparsity problem through minimizing the mutual information loss of the three data matrices based on CCAM.