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

<p>The swift advancement of information technology has catalyzed the emergence of diverse new technical tools, presenting both significant opportunities and formidable challenges for university library service innovation. This study harnesses meta-universe technology to explore its integration with library services and to establish an architecture for an intelligent library system. It employs the trilateration positioning algorithm, among other technologies, to enhance the precision of user-targeted services within the smart library. Furthermore, the similarity of user preferences is calculated using a collaborative filtering algorithm, which is then utilized to refine the system’s recommendation mechanisms. A comprehensive research design was developed, incorporating empirical methods to evaluate the smart library system’s effectiveness from the perspectives of user satisfaction and willingness to engage with the technology. Among the ten higher education institutions evaluated, the ninth institution displayed the most favorable user experience, achieving a score of 0.757. Additionally, the study found a significant positive correlation between the variables measuring users’ willingness to use the system, ranging from 0.1 to 0.7. Notably, the strongest correlation was observed between concerns about privacy and behavioral intent, with a correlation coefficient of 0.6598. These findings underscore the critical importance of prioritizing user privacy in the development of intelligent library systems.</p>
</abstract>

The swift advancement of information technology has catalyzed the emergence of diverse new technical tools, presenting both significant opportunities and formidable challenges for university library service innovation. This study harnesses meta-universe technology to explore its integration with library services and to establish an architecture for an intelligent library system. It employs the trilateration positioning algorithm, among other technologies, to enhance the precision of user-targeted services within the smart library. Furthermore, the similarity of user preferences is calculated using a collaborative filtering algorithm, which is then utilized to refine the system’s recommendation mechanisms. A comprehensive research design was developed, incorporating empirical methods to evaluate the smart library system’s effectiveness from the perspectives of user satisfaction and willingness to engage with the technology. Among the ten higher education institutions evaluated, the ninth institution displayed the most favorable user experience, achieving a score of 0.757. Additionally, the study found a significant positive correlation between the variables measuring users’ willingness to use the system, ranging from 0.1 to 0.7. Notably, the strongest correlation was observed between concerns about privacy and behavioral intent, with a correlation coefficient of 0.6598. These findings underscore the critical importance of prioritizing user privacy in the development of intelligent library systems.

The Role and Function of Artificial Intelligence and the Metaverse in Smart Libraries

Applied Mathematics and Nonlinear Sciences

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

The swift advancement of information technology has catalyzed the emergence of diverse new technical tools, presenting both significant opportunities and...

The Role and Function of Artificial Intelligence and the Metaverse in Smart Libraries

Online veröffentlicht: 02. Juli 2024

Seitenbereich: -

Eingereicht: 02. März 2024

Akzeptiert: 29. Mai 2024

DOI: https://doi.org/10.2478/amns-2024-1578

Schlüsselwörter
Meta-universe, Trilateration Localization Algorithm, Collaborative Filtering, Similarity, Smart Library

© 2024 Xuetao Wang et al., published by Sciendo

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

The Role and Function of Artificial Intelligence and the Metaverse in Smart Libraries

Online veröffentlicht: 02. Juli 2024

Seitenbereich: -

Eingereicht: 02. März 2024

Akzeptiert: 29. Mai 2024

DOI: https://doi.org/10.2478/amns-2024-1578

SchlüsselwörterMeta-universe, Trilateration Localization Algorithm, Collaborative Filtering, Similarity, Smart Library

© 2024 Xuetao Wang et al., published by Sciendo

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

Schlüsselwörter
Meta-universe, Trilateration Localization Algorithm, Collaborative Filtering, Similarity, Smart Library