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Sustainable transparency on recommender systems: Bayesian ranking of images for explainability
dc.contributor.author | Paz Ruza, Jorge | |
dc.contributor.author | Alonso-Betanzos, Amparo | |
dc.contributor.author | Guijarro-Berdiñas, Bertha | |
dc.contributor.author | Cancela, Brais | |
dc.contributor.author | Eiras-Franco, Carlos | |
dc.date.accessioned | 2024-07-05T10:16:20Z | |
dc.date.issued | 2024-11 | |
dc.identifier.citation | J. Paz-Ruza, A. Alonso-Betanzos, B. Guijarro-Berdiñas, B. Cancela, and C. Eiras-Franco, "Sustainable transparency on recommender systems: Bayesian ranking of images for explainability", Information Fusion, Vol. 111, Nov. 2024, article 102497, doi: 10.1016/j.inffus.2024.102497 | es_ES |
dc.identifier.issn | 1566-2535 | |
dc.identifier.uri | http://hdl.handle.net/2183/37744 | |
dc.description.abstract | [Abstract]: Recommender Systems have become crucial in the modern world, commonly guiding users towards relevant content or products, and having a large influence over the decisions of users and citizens. However, ensuring transparency and user trust in these systems remains a challenge; personalized explanations have emerged as a solution, offering justifications for recommendations. Among the existing approaches for generating personalized explanations, using existing visual content created by users is a promising option to maximize transparency and user trust. State-of-the-art models that follow this approach, despite leveraging highly optimized architectures, employ surrogate learning tasks that do not efficiently model the objective of ranking images as explanations for a given recommendation; this leads to a suboptimal training process with high computational costs that may not be reduced without affecting model performance. This work presents BRIE, a novel model where we leverage Bayesian Pairwise Ranking to enhance the training process, allowing us to consistently outperform state-of-the-art models in six real-world datasets while reducing its model size by up to 64 times and its CO2 emissions by up to 75% in training and inference. | es_ES |
dc.description.sponsorship | This research work has been funded by MICIU/AEI /10.13039/501100011033 and ESF+ (grant FPU21/05783), ERDF A way of making Europe (PID2019-109238GB-C22) and ERDF/EU (PID2021-128045OA-I00), and by the Xunta de Galicia, Spain (Grant ED431C 2022/44) with the European Union ERDF funds. CITIC, as Research Center accredited by Galician University System, is funded by “Consellería de Cultura, Educación e Universidade from Xunta de Galicia ”, supported in an 80% through ERDF Operational Programme Galicia 2014–2020, and the remaining 20% by “Secretaría Xeral de Universidades” (Grant ED431G 2019/01). | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C 2022/44 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G 2019/01 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier B.V. | es_ES |
dc.relation | info:eu-repo/grantAgreement/MECD/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/FPU21%2F05783/ES/ | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-109238GB-C22/ES/APRENDIZAJE AUTOMATICO ESCALABLE Y EXPLICABLE | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-128045OA-I00/ES/APRENDIZAJE PROFUNDO ETICO | es_ES |
dc.relation.uri | https://doi.org/10.1016/j.inffus.2024.102497 | es_ES |
dc.rights | Attribution 4.0 International (CC BY) | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | Dyadic data | es_ES |
dc.subject | Explainable Artificial Intelligence | es_ES |
dc.subject | Explainable recommendations | es_ES |
dc.subject | Frugal AI | es_ES |
dc.subject | Machine Learning | es_ES |
dc.subject | Recommender systems | es_ES |
dc.title | Sustainable transparency on recommender systems: Bayesian ranking of images for explainability | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/embargoedAccess | es_ES |
dc.date.embargoEndDate | 2024-11-01 | es_ES |
dc.date.embargoLift | 2024-11-01 | |
UDC.journalTitle | Information Fusion | es_ES |
UDC.volume | 111 | es_ES |
UDC.issue | 102497 | es_ES |
dc.identifier.doi | 10.1016/j.inffus.2024.102497 |
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