  Recent research has shown the advantages of using autoencoders based on deep
neural networks for collaborative filtering. In particular, the recently
proposed Mult-VAE model, which used the multinomial likelihood variational
autoencoders, has shown excellent results for top-N recommendations. In this
work, we propose the Recommender VAE (RecVAE) model that originates from our
research on regularization techniques for variational autoencoders. RecVAE
introduces several novel ideas to improve Mult-VAE, including a novel composite
prior distribution for the latent codes, a new approach to setting the $\beta$
hyperparameter for the $\beta$-VAE framework, and a new approach to training
based on alternating updates. In experimental evaluation, we show that RecVAE
significantly outperforms previously proposed autoencoder-based models,
including Mult-VAE and RaCT, across classical collaborative filtering datasets,
and present a detailed ablation study to assess our new developments. Code and
models are available at https://github.com/ilya-shenbin/RecVAE.
