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Enhancing digital libraries through NLP and recommender systems: current trends and future prospects with large language models

datacite.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
datacite.subject.sdg04:Educação de Qualidade
dc.contributor.authorCardoso, Heike da Silva
dc.contributor.authorRocio, Vitor
dc.date.accessioned2026-01-05T10:53:15Z
dc.date.available2026-01-05T10:53:15Z
dc.date.issued2025
dc.description.abstractIn an era characterized by rapid proliferation of scientific publications and overwhelming volumes of digital content, researchers, students, and faculty members face significant challenges in identifying literature relevant to their academic pursuits. This saturation of information has heightened the need for advanced Recommender Systems within university libraries, tailored specifically for navigating and discovering scientific literature. This paper proposes leveraging insights from librarians’ direct interactions with users to adapt existing Recommender Systems, augmented with NLP and LLMs, to better serve the specific needs of academic researchers. It should streamline the research process by delivering precise, relevant, and personalized literature recommendations, centered on a curated database of bibliographic information.eng
dc.identifier.citationda Silva Cardoso, H., Rocio, V. (2025). Enhancing Digital Libraries Through NLP and Recommender Systems: Current Trends and Future Prospects with Large Language Models. In: Reis, A., et al. Technology and Innovation in Learning, Teaching and Education. TECH-EDU 2024. Communications in Computer and Information Science, vol 2480. Springer, Cham. https://doi.org/10.1007/978-3-032-02672-9_5
dc.identifier.doi10.1007/978-3-032-02672-9_5
dc.identifier.isbn9783032026712
dc.identifier.isbn9783032026729
dc.identifier.issn1865-0929
dc.identifier.issn1865-0937
dc.identifier.urihttp://hdl.handle.net/10400.2/20619
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSpringer Nature Switzerland
dc.relation.ispartofCommunications in Computer and Information Science
dc.relation.ispartofTechnology and Innovation in Learning, Teaching and Education
dc.relation.ispartofseriesCommunications in Computer and Information Science
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/
dc.subjectRecommender Systems
dc.subjectArtificial Intelligence
dc.subjectNatural Language Processing
dc.subjectDigital Libraries
dc.titleEnhancing digital libraries through NLP and recommender systems: current trends and future prospects with large language modelseng
dc.typeconference object
dspace.entity.typePublication
oaire.citation.conferenceDate2025
oaire.citation.endPage79
oaire.citation.startPage66
oaire.citation.titleTechnology and Innovation in Learning, Teaching and Education. TECH-EDU 2024
oaire.citation.volume2480
oaire.versionhttp://purl.org/coar/version/c_b1a7d7d4d402bcce
person.familyNameRocio
person.givenNameVitor
person.identifierR-000-HKF
person.identifier.ciencia-id0418-C5A8-59E2
person.identifier.orcid0000-0002-3314-898X
relation.isAuthorOfPublication7cab4248-456c-46bf-a1cf-bbd212928171
relation.isAuthorOfPublication.latestForDiscovery7cab4248-456c-46bf-a1cf-bbd212928171

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