Deep Variational Autoencoder with Shallow Parallel Path for Top-N Recommendation (VASP)

TL;DR

This paper proposes a novel deep variational autoencoder combined with Neural EASE for top-N recommendation, achieving state-of-the-art results on MovieLens 20M and competitive performance on Netflix datasets.

Contribution

It introduces FLVAE, a deep autoencoder that avoids overfitting, and demonstrates how to train it alongside Neural EASE for improved recommendation accuracy.

Findings

State-of-the-art performance on MovieLens 20M

Competitive results on Netflix Prize dataset

Effective combination of deep VAE with Neural EASE

Abstract

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.