TASK	DATASET	MODEL	METRIC NAME	METRIC VALUE	GLOBAL RANK
Recommendation Systems	MovieLens 20M	VASP	Recall@20	0.414	# 2
Recommendation Systems	MovieLens 20M	VASP	Recall@50	0.552	# 2
Recommendation Systems	MovieLens 20M	VASP	nDCG@100	0.448	# 1

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

10 Feb 2021 · Vojtěch Vančura, Pavel Kordík ·

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.

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