Papers › Deep Variational Autoencoder with Shallow Parallel Path for Top-N Recommendation (VASP)
Deep Variational Autoencoder with Shallow Parallel Path for Top-N Recommendation (VASP)
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.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Recommendation Systems | MovieLens 20M | VASP | Recall@20 | 0.414 | #8 of 18 | Archive leaderboard | report |
| Recommendation Systems | MovieLens 20M | VASP | Recall@50 | 0.552 | #8 of 18 | Archive leaderboard | report |
| Recommendation Systems | MovieLens 20M | VASP | nDCG@100 | 0.448 | #8 of 18 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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