Exploiting user feedbacks in matrix factorization for recommender systems

With the rapid growth of the Web, recommender systems have become essential tools to assist users to find high-quality personalized recommendations from massive information resources. Content-based filtering (CB) and collaborative filtering (CF) are the two most popular and widely used recommendation approaches. In this paper, we focus on ways of taking advantage of both approaches based only on user-item rating data. Motivated by the user profiling technique used in content-based recommendation, we propose to merge user profiles, learnt from the items viewed by the users, as a new latent variable in the latent factor model, which is one of the most popular CF-based approaches, thereby generating more accurate recommendation models. The performance of the proposed models is tested against several widely-deployed state-of-the-art recommendation methods. Experimental results, based on two popular datasets, confirm that better accuracy can be indeed achieved.