Comparative Study of Matrix Factorization Techniques for Personalized Recommender Systems

Abstract

Matrix factorization techniques are generally used in recommender systems to determine latent features of users and items, and to generate personalized recommendations based on these latent features. The main idea behind matrix factorization is to decompose the user-item rating matrix into two lower dimensional matrices - one representing users and the other representing items. The user and item latent factor vectors are then multiplied to generate the predicted ratings. In this paper, we present a comparative analysis of three matrix factorization techniques, UV decomposition, singular value decomposition and CUR algorithm for recommender system. We perform an experimental evaluation to compare the three techniques on two real datasets