Enhancing recommender systems with provider fairness through preference distribution awareness

Going beyond recommendations’ effectiveness, by ensuring properties such as unbiased and fair results, is an aspect that is receiving more and more attention in the literature. This means not only providing accurate recommendations but also ensuring that the visibility of providers aligns with user...

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Autores: Gómez Yepes, Elizabeth, Contreras, David, Boratto, Ludovico, Salamó Llorente, Maria
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Recursos:Universidad de Barcelona
Repositorio:Dipòsit Digital de la UB
OAI Identifier:oai:diposit.ub.edu:2445/219495
Acesso em linha:https://hdl.handle.net/2445/219495
Access Level:acceso abierto
Palavra-chave:Aprenentatge automàtic
Sistemes d'ajuda a la decisió
Ètica
Machine learning
Decision support systems
Ethics
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spelling Enhancing recommender systems with provider fairness through preference distribution awarenessGómez Yepes, ElizabethContreras, DavidBoratto, LudovicoSalamó Llorente, MariaAprenentatge automàticSistemes d'ajuda a la decisióÈticaMachine learningDecision support systemsEthicsGoing beyond recommendations’ effectiveness, by ensuring properties such as unbiased and fair results, is an aspect that is receiving more and more attention in the literature. This means not only providing accurate recommendations but also ensuring that the visibility of providers aligns with user preferences and demographic representation, which has been identified as a key aspect of fairness in recommender systems. In particular, provider fairness enables the generation of results which are equitable for different (groups of) providers. In this paper, we raise the problem of how recommendations are distributed when enabling provider fairness. Indeed, on the one hand, users have clear preferences with respect to which providers they choose (e.g., Italian users mostly buy Italian food), so recommendations should reflect these preferences. On the other hand, content providers should be able to reach a diverse audience, and be visible across the different user groups that expressed a preference for them. Specifically, we consider demographic groups based on their continent of origin for both users and providers, and assess how the preferences of the user groups are distributed across the provider groups. We first show that the state-of-the-art models and the existing approaches that enable provider fairness do not reflect the original distribution of the user preferences. To enable this property, we propose a re-ranking approach that, thanks to the use of buckets associating users and items, favors what we call preference distribution-aware provider fairness. Results on two real-world datasets (i.e., the Book-Crossing and COCO) show that our approach can enable provider fairness and tailor the recommendations to the original distribution of the user preferences, with negligible losses in effectiveness. In particular, in the Books dataset, our approach obtains an overall disparity that is around 6%. On the other hand, in the case of the COCO dataset, the disparities are reduced to 2%.Elsevier2025info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://hdl.handle.net/2445/219495Articles publicats en revistes (Matemàtiques i Informàtica)reponame:Dipòsit Digital de la UBinstname:Universidad de BarcelonaInglésReproducció del document publicat a: https://doi.org/https://doi.org/10.1016/j.jjimei.2024.100311International Journal of Information Management Data Insights, 2025, vol. 5, num.1https://doi.org/https://doi.org/10.1016/j.jjimei.2024.100311cc-by (c) Gómez, E. et al., 2025http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:diposit.ub.edu:2445/2194952026-05-27T06:46:51Z
dc.title.none.fl_str_mv Enhancing recommender systems with provider fairness through preference distribution awareness
title Enhancing recommender systems with provider fairness through preference distribution awareness
spellingShingle Enhancing recommender systems with provider fairness through preference distribution awareness
Gómez Yepes, Elizabeth
Aprenentatge automàtic
Sistemes d'ajuda a la decisió
Ètica
Machine learning
Decision support systems
Ethics
title_short Enhancing recommender systems with provider fairness through preference distribution awareness
title_full Enhancing recommender systems with provider fairness through preference distribution awareness
title_fullStr Enhancing recommender systems with provider fairness through preference distribution awareness
title_full_unstemmed Enhancing recommender systems with provider fairness through preference distribution awareness
title_sort Enhancing recommender systems with provider fairness through preference distribution awareness
dc.creator.none.fl_str_mv Gómez Yepes, Elizabeth
Contreras, David
Boratto, Ludovico
Salamó Llorente, Maria
author Gómez Yepes, Elizabeth
author_facet Gómez Yepes, Elizabeth
Contreras, David
Boratto, Ludovico
Salamó Llorente, Maria
author_role author
author2 Contreras, David
Boratto, Ludovico
Salamó Llorente, Maria
author2_role author
author
author
dc.subject.none.fl_str_mv Aprenentatge automàtic
Sistemes d'ajuda a la decisió
Ètica
Machine learning
Decision support systems
Ethics
topic Aprenentatge automàtic
Sistemes d'ajuda a la decisió
Ètica
Machine learning
Decision support systems
Ethics
description Going beyond recommendations’ effectiveness, by ensuring properties such as unbiased and fair results, is an aspect that is receiving more and more attention in the literature. This means not only providing accurate recommendations but also ensuring that the visibility of providers aligns with user preferences and demographic representation, which has been identified as a key aspect of fairness in recommender systems. In particular, provider fairness enables the generation of results which are equitable for different (groups of) providers. In this paper, we raise the problem of how recommendations are distributed when enabling provider fairness. Indeed, on the one hand, users have clear preferences with respect to which providers they choose (e.g., Italian users mostly buy Italian food), so recommendations should reflect these preferences. On the other hand, content providers should be able to reach a diverse audience, and be visible across the different user groups that expressed a preference for them. Specifically, we consider demographic groups based on their continent of origin for both users and providers, and assess how the preferences of the user groups are distributed across the provider groups. We first show that the state-of-the-art models and the existing approaches that enable provider fairness do not reflect the original distribution of the user preferences. To enable this property, we propose a re-ranking approach that, thanks to the use of buckets associating users and items, favors what we call preference distribution-aware provider fairness. Results on two real-world datasets (i.e., the Book-Crossing and COCO) show that our approach can enable provider fairness and tailor the recommendations to the original distribution of the user preferences, with negligible losses in effectiveness. In particular, in the Books dataset, our approach obtains an overall disparity that is around 6%. On the other hand, in the case of the COCO dataset, the disparities are reduced to 2%.
publishDate 2025
dc.date.none.fl_str_mv 2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2445/219495
url https://hdl.handle.net/2445/219495
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Reproducció del document publicat a: https://doi.org/https://doi.org/10.1016/j.jjimei.2024.100311
International Journal of Information Management Data Insights, 2025, vol. 5, num.1
https://doi.org/https://doi.org/10.1016/j.jjimei.2024.100311
dc.rights.none.fl_str_mv cc-by (c) Gómez, E. et al., 2025
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv cc-by (c) Gómez, E. et al., 2025
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv Articles publicats en revistes (Matemàtiques i Informàtica)
reponame:Dipòsit Digital de la UB
instname:Universidad de Barcelona
instname_str Universidad de Barcelona
reponame_str Dipòsit Digital de la UB
collection Dipòsit Digital de la UB
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812429

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