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

<p>The college library is one of the three pillars of school operation, shouldering the important task of teaching and research services, and is the center of auxiliary teaching, information sharing, academic activities, and cultural inheritance in colleges and universities. The strengths and weaknesses of library construction reflect the level of education and research of the school from one side, and its role in the school construction is self-evident. Therefore, to improve the quality of borrowing by school teachers and students and enhance the utilization rate of library resources. This paper designs a hybrid optimization recommendation algorithm based on the UserCF algorithm and Doc2Vec algorithm in big data to improve the library management and recommendation service. Finally, by summarizing the experimental results, it is concluded that the system running based on this hybrid optimization recommendation algorithm can accurately mine the data of library users’ behavioral preferences, and at the same time can efficiently and precisely recommend the information for readers’ needs. The citation of this optimization algorithm is further demonstrated to achieve the expected results.</p>
</abstract>

The college library is one of the three pillars of school operation, shouldering the important task of teaching and research services, and is the center of auxiliary teaching, information sharing, academic activities, and cultural inheritance in colleges and universities. The strengths and weaknesses of library construction reflect the level of education and research of the school from one side, and its role in the school construction is self-evident. Therefore, to improve the quality of borrowing by school teachers and students and enhance the utilization rate of library resources. This paper designs a hybrid optimization recommendation algorithm based on the UserCF algorithm and Doc2Vec algorithm in big data to improve the library management and recommendation service. Finally, by summarizing the experimental results, it is concluded that the system running based on this hybrid optimization recommendation algorithm can accurately mine the data of library users’ behavioral preferences, and at the same time can efficiently and precisely recommend the information for readers’ needs. The citation of this optimization algorithm is further demonstrated to achieve the expected results.

Research on the innovation path of university library management and service under the background of big data

Inner Mongolia University of Technology, Development Planning and Discipline Construction Office (Higher Education Research Institute)

Applied Mathematics and Nonlinear Sciences

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

The college library is one of the three pillars of school operation, shouldering the important task of teaching and research services, and is the center of...

Research on the innovation path of university library management and service under the background of big data

Online veröffentlicht: 13. Juni 2023

Seitenbereich: -

Eingereicht: 19. Juni 2022

Akzeptiert: 16. Dez. 2022

DOI: https://doi.org/10.2478/amns.2023.1.00387

Schlüsselwörter
Big data, University library, Management and personalized recommendation service, Hybrid recommendation algorithm

© 2023 JiangPing OuYang, published by Sciendo

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

Research on the innovation path of university library management and service under the background of big data

Online veröffentlicht: 13. Juni 2023

Seitenbereich: -

Eingereicht: 19. Juni 2022

Akzeptiert: 16. Dez. 2022

DOI: https://doi.org/10.2478/amns.2023.1.00387

SchlüsselwörterBig data, University library, Management and personalized recommendation service, Hybrid recommendation algorithm

© 2023 JiangPing OuYang, published by Sciendo

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

Schlüsselwörter
Big data, University library, Management and personalized recommendation service, Hybrid recommendation algorithm