A non-IID Framework for Collaborative Filtering with Restricted Boltzmann Machines
Proceedings of the 30th International Conference on Machine Learning, PMLR 28(3):1148-1156, 2013.
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.