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

<p>With the advent of the information era of smart libraries, cloud computing technology provides a new service model and an effective guarantee system for smart libraries. In this paper, we propose a personalized recommendation method and construct a personalized service recommendation model based on collaborative content filtering for the traditional information service model and low service efficiency of university libraries. To improve the accuracy, improvements are made to the VIRE positioning algorithm on the re-districting of virtual labels, the application of the non-linear interpolation method, and the value of K-neighborhood. Optimization strategies are proposed based on the change in the Chinese smart library system, and three aspects are studied: deep change in the legal system, deep change in institutional relationships, and deep change in institutional implementation. Simulation experiments on collaborative filtering recommendation algorithm based on users and items using the Hadoop cloud computing platform show that the accuracy rate remains between 75% and 88% and increases with the smaller MAE value, the more accurate the user recommends books. This study improves the efficiency of library staff and thus is important for the change and development of smart library systems in China.</p>
</abstract>

With the advent of the information era of smart libraries, cloud computing technology provides a new service model and an effective guarantee system for smart libraries. In this paper, we propose a personalized recommendation method and construct a personalized service recommendation model based on collaborative content filtering for the traditional information service model and low service efficiency of university libraries. To improve the accuracy, improvements are made to the VIRE positioning algorithm on the re-districting of virtual labels, the application of the non-linear interpolation method, and the value of K-neighborhood. Optimization strategies are proposed based on the change in the Chinese smart library system, and three aspects are studied: deep change in the legal system, deep change in institutional relationships, and deep change in institutional implementation. Simulation experiments on collaborative filtering recommendation algorithm based on users and items using the Hadoop cloud computing platform show that the accuracy rate remains between 75% and 88% and increases with the smaller MAE value, the more accurate the user recommends books. This study improves the efficiency of library staff and thus is important for the change and development of smart library systems in China.

Changes in China’s smart library system in the information age and how to study it

Library of Linyi University, Linyi University

Applied Mathematics and Nonlinear Sciences

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

With the advent of the information era of smart libraries, cloud computing technology provides a new service model and an effective guarantee system for smart...

Changes in China’s smart library system in the information age and how to study it

Published Online: Sep 09, 2023

Page range: -

Received: Dec 05, 2022

Accepted: Mar 20, 2022

DOI: https://doi.org/10.2478/amns.2023.2.00314

Keywords
Information age, Smart library, Personalized service, VIRE location algorithm, Collaborative filtering algorithm

© 2023 Ruixia Xie, published by Sciendo

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

Changes in China’s smart library system in the information age and how to study it

Published Online: Sep 09, 2023

Page range: -

Received: Dec 05, 2022

Accepted: Mar 20, 2022

DOI: https://doi.org/10.2478/amns.2023.2.00314

KeywordsInformation age, Smart library, Personalized service, VIRE location algorithm, Collaborative filtering algorithm

© 2023 Ruixia Xie, published by Sciendo

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

Keywords
Information age, Smart library, Personalized service, VIRE location algorithm, Collaborative filtering algorithm