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Enhancing digital libraries through NLP and recommender systems: current trends and future prospects with large language models
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Autores
Orientador(es)
Resumo(s)
In 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.
Descrição
Palavras-chave
Recommender Systems Artificial Intelligence Natural Language Processing Digital Libraries
Contexto Educativo
Citação
da 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
Editora
Springer Nature Switzerland