Scalable Approximate NonSymmetric Autoencoder for Collaborative Filtering
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3A10469915" target="_blank" >RIV/00216208:11320/23:10469915 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21340/23:00374207
Result on the web
<a href="https://doi.org/10.1145/3604915.3608827" target="_blank" >https://doi.org/10.1145/3604915.3608827</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1145/3604915.3608827" target="_blank" >10.1145/3604915.3608827</a>
Alternative languages
Result language
angličtina
Original language name
Scalable Approximate NonSymmetric Autoencoder for Collaborative Filtering
Original language description
In the field of recommender systems, shallow autoencoders have recently gained significant attention. One of the most highly acclaimed shallow autoencoders is easer, favored for its competitive recommendation accuracy and simultaneous simplicity. However, the poor scalability of easer (both in time and especially in memory) severely restricts its use in production environments with vast item sets. In this paper, we propose a hyperefficient factorization technique for sparse approximate inversion of the data-Gram matrix used in easer. The resulting autoencoder, sansa, is an end-to-end sparse solution with prescribable density and almost arbitrarily low memory requirements - even for training. As such, sansa allows us to effortlessly scale the concept of easer to millions of items and beyond.
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<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 17th ACM Conference on Recommender Systems
ISBN
979-8-4007-0241-9
ISSN
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e-ISSN
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Number of pages
8
Pages from-to
763-770
Publisher name
ACM
Place of publication
New York, NY, USA
Event location
Singapore, Singapore
Event date
Sep 18, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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