Scalable Linear Shallow Autoencoder for Collaborative Filtering
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F22%3A00360048" target="_blank" >RIV/68407700:21240/22:00360048 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1145/3523227.3551482" target="_blank" >https://doi.org/10.1145/3523227.3551482</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1145/3523227.3551482" target="_blank" >10.1145/3523227.3551482</a>
Alternative languages
Result language
angličtina
Original language name
Scalable Linear Shallow Autoencoder for Collaborative Filtering
Original language description
Recently, the RS research community has witnessed a surge in popularity for shallow autoencoder-based CF methods. Due to its straightforward implementation and high accuracy on item retrieval metrics, EASE is potentially the most prominent of these models. Despite its accuracy and simplicity, EASE cannot be employed in some real-world recommender system applications due to its inability to scale to huge interaction matrices. In this paper, we proposed ELSA, a scalable shallow autoencoder method for implicit feedback recommenders. ELSA is a scalable autoencoder in which the hidden layer is factorizable into a low-rank plus sparse structure, thereby drastically lowering memory consumption and computation time. We conducted a comprehensive offline experimental section that combined synthetic and several real-world datasets. We also validated our strategy in an online setting by comparing ELSA to baselines in a live recommender system using an A/B test. Experiments demonstrate that ELSA is scalable and has competitive performance. Finally, we demonstrate the explainability of ELSA by illustrating the recovered latent space.
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
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
RecSys '22: Proceedings of the 16th ACM Conference on Recommender Systems
ISBN
978-1-4503-9278-5
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
604-609
Publisher name
Association for Computing Machinery
Place of publication
New York
Event location
Seattle
Event date
Sep 18, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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