Contrastive Learning for Explanation Ranking

Simple item page
UDC.coleccionInvestigación
UDC.departamentoCiencias da Computación e Tecnoloxías da Información
UDC.grupoInvLaboratorio de Investigación e Desenvolvemento en Intelixencia Artificial (LIDIA)
UDC.institutoCentroCITIC - Centro de Investigación de Tecnoloxías da Información e da Comunicación
UDC.issue156
UDC.journalTitleMachine Learning
UDC.volume115
dc.contributor.authorEscarda Fernández, Miguel
dc.contributor.authorCancela, Brais
dc.contributor.authorEiras-Franco, Carlos
dc.contributor.authorGuijarro-Berdiñas, Bertha
dc.contributor.authorAlonso-Betanzos, Amparo
dc.date.accessioned2026-07-10T10:08:28Z
dc.date.available2026-07-10T10:08:28Z
dc.date.issued2026
dc.descriptionFinanciado para publicación en acceso aberto: CRUE-CSIC
dc.description.abstract[Abstract]: Explainable recommendation systems enhance user trust and satisfaction by revealing the reasoning behind personalized recommendations. Approaching this as a post-hoc explanation-ranking problem over a fixed pool of candidate explanations, we propose Contrastive Learning for Explanation Ranking (CLER), a model that learns user, item, and explanation representations with a Normalized Temperature-scaled Binary Cross-Entropy (NT-BXent) loss. This function specifically applies a per-row reweighting strategy, preventing the vast number of negative examples from dominating the objective. We evaluate CLER on the Amazon, TripAdvisor, and Yelp datasets from the EXTRA benchmark. Across traditional ranking metrics, CLER achieves the strongest results among the compared baselines.
dc.description.sponsorshipThis work was supported in part by MICIU/AEI/10.13039/501100011033 and ERDF, EU under Grant PID2023-147404OB-I00 and Grant PID2021-128045OA-I00; in part by the Ministry for Digital Transformation and Civil Service and Next-GenerationEU/PRTR under Grant TSI-100925-2023-1; in part by the Xunta de Galicia under Grant ED431C 2022/44; and in part by Consellería de Cultura, Educación e Universidade (ERDF Operational Programme Galicia 2021–2027) and Secretaría Xeral de Universidades under Grant ED431G2023/01.
dc.description.sponsorshipXunta de Galicia; ED431C 2022/44
dc.description.sponsorshipXunta de Galicia; ED431G2023/01
dc.identifier.citationEscarda-Fernández, M., Cancela, B., Eiras-Franco, C. et al. Contrastive Learning for Explanation Ranking. Mach Learn 115, 156 (2026). https://doi.org/10.1007/s10994-026-07099-7
dc.identifier.doi10.1007/s10994-026-07099-7
dc.identifier.issn1573-0565
dc.identifier.urihttps://hdl.handle.net/2183/48855
dc.language.isoeng
dc.publisherSpringer Nature
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2023-147404OB-I00/ES/APRENDIZAJE AUTOMATICO FRUGAL: POTENCIANDO LA IA EN ENTORNOS CON RECURSOS LIMITADOS PARA LOS DESAFIOS DEL MUNDO REAL
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-128045OA-I00/ES/APRENDIZAJE PROFUNDO ETICO
dc.relation.projectIDinfo:eu-repo/grantAgreement/MTDPF//TSI-100925-2023-1/ES/CÁTEDRA UDC-INDITEX DE IA EN ALGORITMOS VERDES
dc.relation.urihttps://doi.org/10.1007/s10994-026-07099-7
dc.rightsAttribution 4.0 Internationalen
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectLearning-to-rank
dc.subjectExplanation recommendation
dc.subjectContrastive learning
dc.subjectPost-hoc explanations
dc.titleContrastive Learning for Explanation Ranking
dc.typejournal article
dc.type.hasVersionVoR
dspace.entity.typePublication
relation.isAuthorOfPublicationba91aca1-bdb4-4be5-b686-463937924910
relation.isAuthorOfPublicationca60a4d3-b38f-4d91-bfa6-f855a8e171ab
relation.isAuthorOfPublicationd839396d-454e-4ccd-9322-d3e89a876865
relation.isAuthorOfPublicationa89f1cad-dbc5-471f-986a-26c021ed4a95
relation.isAuthorOfPublication.latestForDiscoveryba91aca1-bdb4-4be5-b686-463937924910

Files

Original bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
Cancela_Brais_2026_Contrastive_Learning_for_Explanation_Ranking.pdf
Size:
2.82 MB
Format:
Adobe Portable Document Format
Download

Collections

Investigación (FIC)