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

Cardoso, Heike da Silva; Rocio, Vitor

http://hdl.handle.net/10400.2/20619

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Authors

Cardoso, Heike da Silva

Rocio, Vitor

Abstract(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.

Keywords

Recommender Systems Artificial Intelligence Natural Language Processing Digital Libraries

URI

http://hdl.handle.net/10400.2/20619

Citation

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