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RESEARCH ON STUDENT BEHAVIOR INFERENCE METHOD BASED ON FP-GROWTH ALGORITHM

Authors :
J. W. Li
N. Yu
J. W. Jiang
X. Li
Y. Ma
W. D. Chen
Source :
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLII-3-W10, Pp 981-985 (2020)
Publication Year :
2020
Publisher :
Copernicus GmbH, 2020.

Abstract

How to use modern information technology to efficiently and quickly obtain the personalized recommendation information required by students, and to provide high-quality intelligent services for schools, parents and students has become one of the hot issues in college research. This paper uses FP-growth association rule mining algorithm to infer student behavior and then use the collaborative filtering recommendation method to push information according to the inference result, and then push real-time and effective personalized information for students. The experimental results show that an improved FP-growth algorithm is proposed based on the classification of students. The algorithm combines the student behavior inference method of FP-growth algorithm with the collaborative filtering hybrid recommendation method, which not only solves the FP-tree tree branch. Excessive and collaborative filtering recommendation algorithm data sparseness problem, can also analyze different students' behaviors and activities, and accurately push real-time, accurate and effective personalized information for students, to promote smart campus and information intelligence The development provides better service.

Details

ISSN :
21949034
Database :
OpenAIRE
Journal :
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Accession number :
edsair.doi.dedup.....449ed737afc0b5748a2f9ea79e753b81
Full Text :
https://doi.org/10.5194/isprs-archives-xlii-3-w10-981-2020