<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This study explores the application of extensive data analysis to library information resources, focusing on enhancing the efficiency of resource utilization and user satisfaction through personalized recommendation services. We employ cluster analysis algorithms, and least squares support vector machines to model library users’ borrowing behaviors. Additionally, we use the Apriori algorithm and collaborative filtering to recommend books effectively. This study’s experimental outcomes show a notable 11.36% increase in the index of network resource demand satisfaction, indicating a significant enhancement in the library’s ability to meet resource update demands. The adoption of big data analytics has been instrumental in advancing library information management and expanding extensive data services. The findings underscore the pivotal role of big data in enhancing the efficiency of information resource utilization and elevating user satisfaction, mainly through personalized recommendations.</p>
</abstract>

This study explores the application of extensive data analysis to library information resources, focusing on enhancing the efficiency of resource utilization and user satisfaction through personalized recommendation services. We employ cluster analysis algorithms, and least squares support vector machines to model library users’ borrowing behaviors. Additionally, we use the Apriori algorithm and collaborative filtering to recommend books effectively. This study’s experimental outcomes show a notable 11.36% increase in the index of network resource demand satisfaction, indicating a significant enhancement in the library’s ability to meet resource update demands. The adoption of big data analytics has been instrumental in advancing library information management and expanding extensive data services. The findings underscore the pivotal role of big data in enhancing the efficiency of information resource utilization and elevating user satisfaction, mainly through personalized recommendations.

Big data analysis and application of library information resources

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Big data analysis and application of library information resources"}

This study explores the application of extensive data analysis to library information resources, focusing on enhancing the efficiency of resource utilization...

Big data analysis and application of library information resources

Online veröffentlicht: 30. Mai 2024

Seitenbereich: -

Eingereicht: 25. Feb. 2024

Akzeptiert: 02. Mai 2024

DOI: https://doi.org/10.2478/amns-2024-1212

Schlüsselwörter
Big Data Analytics, Web Resources, Personalized Recommendations, User Satisfaction

© 2024 Xiuwen Zhang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Big data analysis and application of library information resources

Online veröffentlicht: 30. Mai 2024

Seitenbereich: -

Eingereicht: 25. Feb. 2024

Akzeptiert: 02. Mai 2024

DOI: https://doi.org/10.2478/amns-2024-1212

SchlüsselwörterBig Data Analytics, Web Resources, Personalized Recommendations, User Satisfaction

© 2024 Xiuwen Zhang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Big Data Analytics, Web Resources, Personalized Recommendations, User Satisfaction