Deep learning algorithms for personalized services and enhanced user experience in libraries: a systematic review / Haziah Sa’ari, Mohd Dasuki Sahak and Stan Skrzeszewskis

• Main Authors: Sa’ari, Haziah, Sahak, Mohd Dasuki, Skrzeszewskis, Stan
• Format: Article
• Language: English
• Published: Universiti Teknologi MARA, Perak 2023
• Subjects: QA Mathematics; Algorithms
• Online Access: https://ir.uitm.edu.my/id/eprint/88207/1/88207.pdf; https://ir.uitm.edu.my/id/eprint/88207/

The integration of deep learning (DL) algorithms in library settings engenders a multitude of challenges and complexities, encompassing unintended ramifications, ethical quandaries, a dearth of specialized literature elucidating DL in library contexts, the intricacies of dataset selection and human intervention, and the inherent limitations when juxtaposed with the remarkable cognitive capabilities of the human brain. To surmount these hurdles and attain a profound comprehension of DL in library settings, a rigorous and comprehensive systematic literature review (SLR) becomes imperative. This study investigates the application of DL algorithms in examining user-seeking behaviour to provide personalized services and enhance user experience in libraries. Through a comprehensive literature review, the study aims to uncover the benefits, challenges, and implications of integrating DL algorithms for user behaviour analysis and personalized services in library environments. The investigation encompasses a systematic literature review, employing a meticulous search and screening process utilizing the Scopus database. DL algorithms enable tailored recommendations, resource suggestions, and personalized search outcomes, improving information retrieval and user-centric services. Ethical considerations and ongoing research are emphasized to address challenges and maximize the potential of DL algorithms in libraries. The integration of DL algorithms in libraries yields substantial benefits, including improved information retrieval capabilities, augmented resource recommendation systems, and the delivery of user-centric services. The paper offers valuable insights to researchers, practitioners, and stakeholders operating within this field.