Proceedings of the 2023 4th International Conference on Artificial Intelligence and Education (ICAIE 2023)

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Based on CHAID’s Knowledge Base Uses Expectations and Intelligent Recommendations

Authors
Kuotai Tang1, *, Hailing Duan2, Lin Fang1, Xiaowei Huang2
1Department of Business Management, Fujian Polytechnic of Information Technology, Fuzhou, China
2School of Internet Economics and Business, Fujian University of Technology, Fuzhou, China
*Corresponding author. Email: 3224243499@qq.com
Corresponding Author
Kuotai Tang
Available Online 22 September 2023.
DOI
10.2991/978-94-6463-242-2_70How to use a DOI?
Keywords
CHAID; TAM; Knowledge Base
Abstract

Knowledge base is an important part of cultural soft power, and the expected use of knowledge data is often the concern of knowledge base operators, and also the expectation of college students when using it. In this paper, a questionnaire was constructed based on the technology acceptance model (TAM). A total of 319 valid questionnaires were received. Cronbach α = 0.965(>0.9), KMO = 0.576(>0.5), indicating that it has the significance of in-depth research. In this paper, the CHAID algorithm is used to mine the data and explore the matching mode between college students’ data usage demand and usage expectation. The results show that the use of knowledge base is often reflected in the acquisition of skills and knowledge (P = 0.001); Most students are more dependent on paid knowledge (P = 0.002); Real-time information apps are favored by college students (P = 0.005); Video knowledge is not only favored by college students but also willing to share (P = 0.023). Finally, it is suggested that knowledge base operators can strengthen the application of intelligent recommendation, and match real-time according to user characteristics to achieve the purpose of knowledge service.

Copyright
© 2024 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.

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Volume Title
Proceedings of the 2023 4th International Conference on Artificial Intelligence and Education (ICAIE 2023)
Series
Atlantis Highlights in Computer Sciences
Publication Date
22 September 2023
ISBN
10.2991/978-94-6463-242-2_70
ISSN
2589-4900
DOI
10.2991/978-94-6463-242-2_70How to use a DOI?
Copyright
© 2024 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

risenwbib
TY  - CONF
AU  - Kuotai Tang
AU  - Hailing Duan
AU  - Lin Fang
AU  - Xiaowei Huang
PY  - 2023
DA  - 2023/09/22
TI  - Based on CHAID’s Knowledge Base Uses Expectations and Intelligent Recommendations
BT  - Proceedings of the 2023 4th International Conference on Artificial Intelligence and Education (ICAIE 2023)
PB  - Atlantis Press
SP  - 573
EP  - 581
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-242-2_70
DO  - 10.2991/978-94-6463-242-2_70
ID  - Tang2023
ER  -