Deep Variational Autoencoder with Shallow Parallel Path for Top-N Recommendation (VASP)
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F21%3A00351311" target="_blank" >RIV/68407700:21240/21:00351311 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/978-3-030-86383-8_11" target="_blank" >https://doi.org/10.1007/978-3-030-86383-8_11</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-86383-8_11" target="_blank" >10.1007/978-3-030-86383-8_11</a>
Alternative languages
Result language
angličtina
Original language name
Deep Variational Autoencoder with Shallow Parallel Path for Top-N Recommendation (VASP)
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA18-18080S" target="_blank" >GA18-18080S: Fusion-Based Knowledge Discovery in Human Activity Data</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Artificial Neural Networks and Machine Learning – ICANN 2021
ISBN
978-3-030-86383-8
ISSN
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e-ISSN
1611-3349
Number of pages
12
Pages from-to
138-149
Publisher name
Springer
Place of publication
Cham
Event location
Bratislava
Event date
Sep 14, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000711936300011