Matrix Factorization with Neural Networks and Stochastic Variational Inference
In this paper, we explore the "Neural Network Matrix Factorization" (NNMF) model [1] which uses a neural network to predict movie ratings as a function of user and movie latent variables. We extend this model by using variational Bayesian inference to approximate the posterior distributions of the latent variables. To train the model, named "SVINNMF" we use stochastic variational inference, where stochastic gradients of likelihood and KL divergence terms can be computed on a small number of sample ratings (rather than the entire dataset) Learning posterior distributions of the latent variables allows us to express a degree of certainty about what we've learned about users and movies, and also allows us to predict a reasonable range of estimated ratings for a given user/movie pair. This provides the flexibility to recommender systems to make decisions based on the uncertainty of predicted ratings. Both the NNMF and SVINNMF models were evaluated against the MovieLens datasets, and compared to PMF and BPMF as baseline models.