Integrating content-based filtering with collaborative filtering using co-clustering with augmented matrices

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. s 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.