Back to Search
Start Over
RESEARCH ON STUDENT BEHAVIOR INFERENCE METHOD BASED ON FP-GROWTH ALGORITHM
- 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.
- Subjects :
- lcsh:Applied optics. Photonics
Service (systems architecture)
Association rule learning
lcsh:T
Computer science
business.industry
lcsh:TA1501-1820
Inference
Information technology
lcsh:Technology
Tree (data structure)
lcsh:TA1-2040
Collaborative filtering
lcsh:Engineering (General). Civil engineering (General)
business
Algorithm
Subjects
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