Analysis of Cognitive Factors Affecting Information Retrieval Behavior of University Library Users Based on RBF Network
- DOI
- 10.2991/978-94-6463-172-2_118How to use a DOI?
- Keywords
- Information retrieval behavior; University library; RBF neural network; Cognitive factors
- Abstract
The information retrieval behavior of library users is affected by many factors. Analysis of these factors will help libraries to formulate high-quality information service strategies. Sample data is obtained by questionnaire survey, and RBF neural network model is used to learn and analyze sample data in this paper. A method for calculating the importance of input components based on RBF neural network is proposed. The influence factors of university library users’ information retrieval behavior are studied from a cognitive perspective. The influence results of relevant cognitive factors on information retrieval behavior are discussed. According to these cognitive factors, university libraries can take corresponding information service measures and improve the information retrieval efficiency of library users.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Ping Wang AU - Yan Chen PY - 2023 DA - 2023/06/30 TI - Analysis of Cognitive Factors Affecting Information Retrieval Behavior of University Library Users Based on RBF Network BT - Proceedings of the 2023 4th International Conference on Education, Knowledge and Information Management (ICEKIM 2023) PB - Atlantis Press SP - 1127 EP - 1133 SN - 2589-4900 UR - https://doi.org/10.2991/978-94-6463-172-2_118 DO - 10.2991/978-94-6463-172-2_118 ID - Wang2023 ER -