Deep learning algorithms for personalized services and enhanced user experience in libraries

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...

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Main Authors: Sa'ari, Haziah, Sahak, Mohd Dasuki, Skrzeszewskis, Stan
Format: Article
Published: UiTM Press, Universiti Teknologi MARA 2023
Online Access:http://psasir.upm.edu.my/id/eprint/111136/
https://ir.uitm.edu.my/id/eprint/88207/1/88207.pdf
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Institution: Universiti Putra Malaysia
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spelling my.upm.eprints.1111362024-06-17T08:31:34Z http://psasir.upm.edu.my/id/eprint/111136/ Deep learning algorithms for personalized services and enhanced user experience in libraries Sa'ari, Haziah Sahak, Mohd Dasuki Skrzeszewskis, Stan 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. UiTM Press, Universiti Teknologi MARA 2023 Article PeerReviewed Sa'ari, Haziah and Sahak, Mohd Dasuki and Skrzeszewskis, Stan (2023) Deep learning algorithms for personalized services and enhanced user experience in libraries. Mathematical Sciences and Informatics Journal, 4 (1). pp. 30-47. ISSN 2735-0703 https://ir.uitm.edu.my/id/eprint/88207/1/88207.pdf 10.24191/mij.v4i2.23026
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
description 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.
format Article
author Sa'ari, Haziah
Sahak, Mohd Dasuki
Skrzeszewskis, Stan
spellingShingle Sa'ari, Haziah
Sahak, Mohd Dasuki
Skrzeszewskis, Stan
Deep learning algorithms for personalized services and enhanced user experience in libraries
author_facet Sa'ari, Haziah
Sahak, Mohd Dasuki
Skrzeszewskis, Stan
author_sort Sa'ari, Haziah
title Deep learning algorithms for personalized services and enhanced user experience in libraries
title_short Deep learning algorithms for personalized services and enhanced user experience in libraries
title_full Deep learning algorithms for personalized services and enhanced user experience in libraries
title_fullStr Deep learning algorithms for personalized services and enhanced user experience in libraries
title_full_unstemmed Deep learning algorithms for personalized services and enhanced user experience in libraries
title_sort deep learning algorithms for personalized services and enhanced user experience in libraries
publisher UiTM Press, Universiti Teknologi MARA
publishDate 2023
url http://psasir.upm.edu.my/id/eprint/111136/
https://ir.uitm.edu.my/id/eprint/88207/1/88207.pdf
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