beeFormer: Bridging the Gap Between Semantic and Interaction Similarity in Recommender Systems
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F24%3A10492912" target="_blank" >RIV/00216208:11320/24:10492912 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21240/24:00377673
Result on the web
<a href="https://doi.org/10.1145/3640457.3691707" target="_blank" >https://doi.org/10.1145/3640457.3691707</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1145/3640457.3691707" target="_blank" >10.1145/3640457.3691707</a>
Alternative languages
Result language
angličtina
Original language name
beeFormer: Bridging the Gap Between Semantic and Interaction Similarity in Recommender Systems
Original language description
Recommender systems often use text-side information to improve their predictions, especially in cold-start or zero-shot recommendation scenarios, where traditional collaborative filtering approaches cannot be used. Many approaches to text-mining side information for recommender systems have been proposed over recent years, with sentence Transformers being the most prominent one. However, these models are trained to predict semantic similarity without utilizing interaction data with hidden patterns specific to recommender systems. In this paper, we propose beeFormer, a framework for training sentence Transformer models with interaction data. We demonstrate that our models trained with beeFormer can transfer knowledge between datasets while outperforming not only semantic similarity sentence Transformers but also traditional collaborative filtering methods. We also show that training on multiple datasets from different domains accumulates knowledge in a single model, unlocking the possibility of trainin
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/GX20-16819X" target="_blank" >GX20-16819X: Language Understanding: from Syntax to Discourse</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2024
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 18th ACM Conference on Recommender Systems
ISBN
979-8-4007-0505-2
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
1102-1107
Publisher name
Association for Computing Machinery
Place of publication
New York, NY, United States
Event location
Bari, Italy
Event date
Sep 14, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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