A non IID Framework for Collaborative Filtering with Restricted Boltzmann Machines
We propose a framework for collaborative filtering based on Restricted Boltzmann Machines (RBM) which extends previous RBM based approaches in several important directions. First, while previous RBM research has focused on modeling the correlation between item ratings, we model both user user and item item correlations in a unified hybrid non IID framework. We further use real values in the visible layer as opposed to multinomial variables, thus taking advantage of the natural order between user item ratings. Finally, we explore the potential of combining the original training data with data generated by the RBM based model itself in a bootstrapping fashion. The evaluation on two MovieLens datasets (with 100K and 1M user item ratings, respectively) shows that our RBM model rivals the best previously proposed approaches.
ICML