Collaborative Filtering with CCAM

Recommender system has become an important research topic since the high interest of academia and industry. 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 a problem of sparsity which is caused by relevantly less number of ratings against the unknowns that need to be predicted. In this paper, we consider a hybrid approach which combines the content-based approach with collaborative filtering under a unified model called Co-Clustering with Augmented data Matrix (CCAM). CCAM is based on information-theoretic co-clustering but further considers augmented data matrix like user profile and item description. By presenting results on a better error of prediction, we show that our algorithm is more effective in addressing sparsity through optimizing the co-cluster in mutual information loss between multiple tabular data than algorithm with single data and algorithms do not consider mutual information loss or co-clustering in our prediction framework.