A Modified PMF Model Incorporating Implicit Item Associations

Abstract

As a state-of-the-art recommendation technique, collaborative filtering (CF) methods compute recommendations by leveraging a historical data set of users' ratings for items. So far, the best performing CF methods are latent factor models. Probabilistic matrix factorization (PMF) model, as a widely used latent factor model, offers a probabilistic foundation for regularization. In this paper, we present a novel CF method by incorporating implicit relationship between items into the basic PMF model. Firstly we mine the implicit correlation between items based on a matrix factorization model by utilizing contextual information, and then generalize recommendations by incorporating the obtained item relationship into the basic PMF model. We validate our approach on two datasets, and the experimental results show that the proposed method outperforms several existing CF models.