Discovering latent factors from movies genres for enhanced recommendation

Abstract

Current approaches on collaborative filtering factorize user-item matrices in order to infer latent factors from ratings previously assigned by users. However, they all have to deal with sparseness, whose workarounds are prone to bias and/or overfitting. This paper proposes a recommender algorithm that is based on a factorized matrix composed of user preferences associated to the movies' genres/categories. The advantage of using such user-genre matrix factorization model is that it requires less computational resources, as the matrix will be less sparse and at lower dimension. We present the experimental results with a dataset composed of real users, comparing the performance of different modules of our algorithm.