Proceedings of the 2019 International Conference on Organizational Innovation (ICOI 2019)

A Novel Method of Applying Big Data for Analysis Model of Library User Behavior

Authors
Kaijun Yu, Song Luo, Xuejun Zhou, Rui Wang, Longjie Sun
Corresponding Author
Kaijun Yu
Available Online October 2019.
DOI
10.2991/icoi-19.2019.130How to use a DOI?
Keywords
Data mining, Supervised learning, User portrait
Abstract

A large number of library user behaviour data generated in real time in the era of big data artificial intelligence requires more efficient and scientific analysis technology to help libraries improve the level and quality of personalized services, while the increasingly popular campus Internet of Things system needs to be more Active network security precautions, proactively detect unreliable abnormal behavior of the network and feedback users to improve security awareness. Explores a big data analysis model using traditional data mining and classification learning, which combines user personality analysis and abnormal behavior detection

Copyright
© 2019, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2019 International Conference on Organizational Innovation (ICOI 2019)
Series
Advances in Economics, Business and Management Research
Publication Date
October 2019
ISBN
978-94-6252-806-2
ISSN
2352-5428
DOI
10.2991/icoi-19.2019.130How to use a DOI?
Copyright
© 2019, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Kaijun Yu
AU  - Song Luo
AU  - Xuejun Zhou
AU  - Rui Wang
AU  - Longjie Sun
PY  - 2019/10
DA  - 2019/10
TI  - A Novel Method of Applying Big Data for Analysis Model of Library User Behavior
BT  - Proceedings of the 2019 International Conference on Organizational Innovation (ICOI 2019)
PB  - Atlantis Press
SP  - 742
EP  - 745
SN  - 2352-5428
UR  - https://doi.org/10.2991/icoi-19.2019.130
DO  - 10.2991/icoi-19.2019.130
ID  - Yu2019/10
ER  -