  Neural network based models for collaborative filtering have started to gain
attention recently. One branch of research is based on using deep generative
models to model user preferences where variational autoencoders were shown to
produce state-of-the-art results. However, there are some potentially
problematic characteristics of the current variational autoencoder for CF. The
first is the too simplistic prior that VAEs incorporate for learning the latent
representations of user preference. The other is the model's inability to learn
deeper representations with more than one hidden layer for each network. Our
goal is to incorporate appropriate techniques to mitigate the aforementioned
problems of variational autoencoder CF and further improve the recommendation
performance. Our work is the first to apply flexible priors to collaborative
filtering and show that simple priors (in original VAEs) may be too restrictive
to fully model user preferences and setting a more flexible prior gives
significant gains. We experiment with the VampPrior, originally proposed for
image generation, to examine the effect of flexible priors in CF. We also show
that VampPriors coupled with gating mechanisms outperform SOTA results
including the Variational Autoencoder for Collaborative Filtering by meaningful
margins on 2 popular benchmark datasets (MovieLens & Netflix).
