A Neural Autoregressive Framework for Collaborative Filtering

Restricted Boltzmann Machine (RBM) is a two layer undirected graph model that capable to represent complex distributions. Recent research has shown RBM-based approach has comparable performance with or even better performance than previous models on many collaborative filtering (CF) tasks. However, the intractable inference makes the training of RBM sophisticated, which prevents it from practical application. We present a novel feedforward neural framework for collaborative filtering called NACF, which is extended from the Neural Autoregressive Distribution Estimator (NADE). Because of the autoregressive feed-forward architecture, NACF can be trained with efficient stochastic gradient descent, instead of slow and hard-to-tune truncated Gibbs sampling for RBM. By introducing linear visible units and dual reversed ordering, NACF show faster convergence and better results than Probabilistic Matrix Factorization (PMF) and corresponding RBM models on MovieLens dataset. Besides, by combining NACF results, the rating prediction of efficientsignificantly improved, showing NACF is an effective and efficient model for collaborative filtering.