Deep Variational Autoencoder with Shallow Parallel Path for Top-N Recommendation (VASP)
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 10 of its 26 references that are in the map. First posted on arXiv 10 Feb 2021.
Builds on
10 of 26 references.
References · 10
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2016Densely Connected Convolutional Networkscited by 6,691
- 2013Auto-Encoding Variational Bayescited by 3,980
- 2016Wide & Deep Learning for Recommender Systemscited by 745
- 2018Variational Autoencoders for Collaborative Filteringcited by 308
- 2016Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Predictioncited by 217
- 2017Deep Learning based Recommender System: A Survey and New Perspectivescited by 136
- 2019Embarrassingly Shallow Autoencoders for Sparse Datacited by 93
- 2019RecVAE: a New Variational Autoencoder for Top-N Recommendations with Implicit Feedbackcited by 63
- 2019Enhancing VAEs for Collaborative Filtering: Flexible Priors & Gating Mechanismscited by 8
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Citing papers per year: 2024: 2; 2025: 1.
3 papers.