Ing. Vojtěch Vančura


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

Vančura, V.; Kordík, P.
Artificial Neural Networks and Machine Learning – ICANN 2021. Cham: Springer, 2021. p. 138-149. V. vol. 12895. ISSN 1611-3349. ISBN 978-3-030-86383-8.
Stať ve sborníku
The recently introduced Embarrasingelly Shallow Autoencoder (EASE) algorithm presents a simple and elegant way 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 Focal Loss Variational 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 state-of-the-art performance on the MovieLens 20M dataset and competitive results on the Netflix Prize dataset.