Bias Assessment Approaches for Addressing User-Centered Fairness in GNN-Based Recommender Systems

[EN]In today’s technology-driven society, many decisions are made based on the results provided by machine learning algorithms. It is widely known that the models generated by such algorithms may present biases that lead to unfair decisions for some segments of the population, such as minority or ma...

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Autores: Chizari, Nikzad, Tajfar, Keywan, Moreno García, María Navelonga
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2023
País:España
Institución:Universidad de Salamanca (USAL)
Repositorio:GREDOS. Repositorio Institucional de la Universidad de Salamanca
OAI Identifier:oai:gredos.usal.es:10366/166825
Acceso en línea:http://hdl.handle.net/10366/166825
Access Level:acceso abierto
Palabra clave:Recommender systems
GNN (Graph Neural Networks)
Bias
Fairness
Sensitive features
1203 Ciencia de los ordenadores
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oai_identifier_str oai:gredos.usal.es:10366/166825
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spelling Bias Assessment Approaches for Addressing User-Centered Fairness in GNN-Based Recommender SystemsChizari, NikzadTajfar, KeywanMoreno García, María NavelongaRecommender systemsGNN (Graph Neural Networks)BiasFairnessSensitive features1203 Ciencia de los ordenadores[EN]In today’s technology-driven society, many decisions are made based on the results provided by machine learning algorithms. It is widely known that the models generated by such algorithms may present biases that lead to unfair decisions for some segments of the population, such as minority or marginalized groups. Hence, there is concern about the detection and mitigation of these biases, which may increase the discriminatory treatments of some demographic groups. Recommender systems, used today by millions of users, are not exempt from this drawback. The influence of these systems on so many user decisions, which in turn are taken as the basis for future recommendations, contributes to exacerbating this problem. Furthermore, there is evidence that some of the most recent and successful recommendation methods, such as those based on graphical neural networks (GNNs), are more sensitive to bias. The evaluation approaches of some of these biases, as those involving protected demographic groups, may not be suitable for recommender systems since their results are the preferences of the users and these do not necessarily have to be the same for the different groups. Other assessment metrics are aimed at evaluating biases that have no impact on the user. In this work, the suitability of different user-centered bias metrics in the context of GNN-based recommender systems are analyzed, as well as the response of recommendation methods with respect to the different types of biases to which these measures are addressed.MDPI202520252023info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10366/166825reponame:GREDOS. Repositorio Institucional de la Universidad de Salamancainstname:Universidad de Salamanca (USAL)InglésAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:gredos.usal.es:10366/1668252026-06-07T06:28:51Z
dc.title.none.fl_str_mv Bias Assessment Approaches for Addressing User-Centered Fairness in GNN-Based Recommender Systems
title Bias Assessment Approaches for Addressing User-Centered Fairness in GNN-Based Recommender Systems
spellingShingle Bias Assessment Approaches for Addressing User-Centered Fairness in GNN-Based Recommender Systems
Chizari, Nikzad
Recommender systems
GNN (Graph Neural Networks)
Bias
Fairness
Sensitive features
1203 Ciencia de los ordenadores
title_short Bias Assessment Approaches for Addressing User-Centered Fairness in GNN-Based Recommender Systems
title_full Bias Assessment Approaches for Addressing User-Centered Fairness in GNN-Based Recommender Systems
title_fullStr Bias Assessment Approaches for Addressing User-Centered Fairness in GNN-Based Recommender Systems
title_full_unstemmed Bias Assessment Approaches for Addressing User-Centered Fairness in GNN-Based Recommender Systems
title_sort Bias Assessment Approaches for Addressing User-Centered Fairness in GNN-Based Recommender Systems
dc.creator.none.fl_str_mv Chizari, Nikzad
Tajfar, Keywan
Moreno García, María Navelonga
author Chizari, Nikzad
author_facet Chizari, Nikzad
Tajfar, Keywan
Moreno García, María Navelonga
author_role author
author2 Tajfar, Keywan
Moreno García, María Navelonga
author2_role author
author
dc.subject.none.fl_str_mv Recommender systems
GNN (Graph Neural Networks)
Bias
Fairness
Sensitive features
1203 Ciencia de los ordenadores
topic Recommender systems
GNN (Graph Neural Networks)
Bias
Fairness
Sensitive features
1203 Ciencia de los ordenadores
description [EN]In today’s technology-driven society, many decisions are made based on the results provided by machine learning algorithms. It is widely known that the models generated by such algorithms may present biases that lead to unfair decisions for some segments of the population, such as minority or marginalized groups. Hence, there is concern about the detection and mitigation of these biases, which may increase the discriminatory treatments of some demographic groups. Recommender systems, used today by millions of users, are not exempt from this drawback. The influence of these systems on so many user decisions, which in turn are taken as the basis for future recommendations, contributes to exacerbating this problem. Furthermore, there is evidence that some of the most recent and successful recommendation methods, such as those based on graphical neural networks (GNNs), are more sensitive to bias. The evaluation approaches of some of these biases, as those involving protected demographic groups, may not be suitable for recommender systems since their results are the preferences of the users and these do not necessarily have to be the same for the different groups. Other assessment metrics are aimed at evaluating biases that have no impact on the user. In this work, the suitability of different user-centered bias metrics in the context of GNN-based recommender systems are analyzed, as well as the response of recommendation methods with respect to the different types of biases to which these measures are addressed.
publishDate 2023
dc.date.none.fl_str_mv 2023
2025
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 http://hdl.handle.net/10366/166825
url http://hdl.handle.net/10366/166825
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv Attribution-NonCommercial-NoDerivatives 4.0 Internacional
http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution-NonCommercial-NoDerivatives 4.0 Internacional
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:GREDOS. Repositorio Institucional de la Universidad de Salamanca
instname:Universidad de Salamanca (USAL)
instname_str Universidad de Salamanca (USAL)
reponame_str GREDOS. Repositorio Institucional de la Universidad de Salamanca
collection GREDOS. Repositorio Institucional de la Universidad de Salamanca
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.81155

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