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

<p>This paper first proposes a real-time recommendation technique based on user images and utilizes multi-dimensional small data fusion for user image construction, which in turn gives a personalized wisdom service recommendation structure. Secondly, user long and short-term preference fusion is carried out by introducing a self-attention mechanism and GRU network, and then personalized retrieval recommendation based on user long and short-term preference is realized. Finally, an investigation is carried out to determine the effectiveness of the personalized wisdom information service model through empirical analysis. The results show that the mean value of literature and history in the user portrait category III is 1.553, the change in the borrowing of books in the I major category decreased by 31.94% from 2018 to 2022, and the average satisfaction of users for the personalized recommendation service is 83.52%. This shows that the personalized intelligent information service model of the college library can realize the innovation of library information service and further improve the satisfaction of users for the college libraries.</p>
</abstract>

This paper first proposes a real-time recommendation technique based on user images and utilizes multi-dimensional small data fusion for user image construction, which in turn gives a personalized wisdom service recommendation structure. Secondly, user long and short-term preference fusion is carried out by introducing a self-attention mechanism and GRU network, and then personalized retrieval recommendation based on user long and short-term preference is realized. Finally, an investigation is carried out to determine the effectiveness of the personalized wisdom information service model through empirical analysis. The results show that the mean value of literature and history in the user portrait category III is 1.553, the change in the borrowing of books in the I major category decreased by 31.94% from 2018 to 2022, and the average satisfaction of users for the personalized recommendation service is 83.52%. This shows that the personalized intelligent information service model of the college library can realize the innovation of library information service and further improve the satisfaction of users for the college libraries.

Information Service Innovation in College Libraries Based on Big Data Analysis

Applied Mathematics and Nonlinear Sciences

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

{"article-title":"Information Service Innovation in College Libraries Based on Big Data Analysis"}

This paper first proposes a real-time recommendation technique based on user images and utilizes multi-dimensional small data fusion for user image...

Information Service Innovation in College Libraries Based on Big Data Analysis

Data publikacji: 22 lis 2023

Zakres stron: -

Otrzymano: 02 mar 2023

Przyjęty: 02 cze 2023

DOI: https://doi.org/10.2478/amns.2023.2.01212

Słowa kluczowe
Fully connected networks, Personalized recommendations, Self-attention, Libraries, Information services

© 2023 Zhuyu Li, published by Sciendo

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

Information Service Innovation in College Libraries Based on Big Data Analysis

Data publikacji: 22 lis 2023

Zakres stron: -

Otrzymano: 02 mar 2023

Przyjęty: 02 cze 2023

DOI: https://doi.org/10.2478/amns.2023.2.01212

Słowa kluczoweFully connected networks, Personalized recommendations, Self-attention, Libraries, Information services

© 2023 Zhuyu Li, published by Sciendo

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

Słowa kluczowe
Fully connected networks, Personalized recommendations, Self-attention, Libraries, Information services