Abstract
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.
Highlights
In recent years, with the development of “digital campus” and “smart campus” in colleges and universities, the core role of card in campus has become more and more prominent [1]
As a must-have item for teachers and students, the campus card records related data such as student dining, borrowing books, supermarket access control, and online data. These data hide the information of most students' daily behaviors, and through data mining and Analysis of students' daily behavior can inform students' academic status in advance [2]
This paper proposes a behavior inference method based on FP-growth algorithm for the defects of the above algorithms
Summary
With the development of “digital campus” and “smart campus” in colleges and universities, the core role of card in campus has become more and more prominent [1]. As a must-have item for teachers and students, the campus card records related data such as student dining, borrowing books, supermarket access control, and online data. These data hide the information of most students' daily behaviors, and through data mining and Analysis of students' daily behavior can inform students' academic status in advance [2]. Research method, which analyzes popular activities/information and high-quality activities/information through log analysis, records relevant data information, and uses the improved FP-growth association rule mining algorithm to push information inference results to users in real time. Filtering the recommendation data sparseness problem while providing students with real-time, effective personalized activities/information push that meets their needs
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