  Recently introduced EASE algorithm presents a simple and elegant way, how to
solve the top-N recommendation task. In this paper, we introduce Neural EASE to
further improve the performance of this algorithm by incorporating techniques
for training modern neural networks. Also, there is a growing interest in the
recsys community to utilize variational autoencoders (VAE) for this task. We
introduce deep autoencoder FLVAE benefiting from multiple non-linear layers
without an information bottleneck while not overfitting towards the identity.
We show how to learn FLVAE in parallel with Neural EASE and achieve the state
of the art performance on the MovieLens 20M dataset and competitive results on
the Netflix Prize dataset.
