Research on User Profile Recommendation Algorithm Based on Learning Situation Data
HUANG Chengning
SUN Jie
ZHU Yuquan
Abstract：In order to achieve intelligent recommendation of personalized and accurate teaching resources,this paper has deep-ly studied the fusion model of algorithms.It combines educational administration and learning situation data with personalized recom-mendation,excavates feature information from student portraits,and achieves more accurate and personalized independent learning recommendation.This paper focuses on the research of personalized recommendation algorithms.The three categories of binary algo-rithms,namely SVM,LR and RF,are considered as three single machine learning methods.They are fused into a new strong learn-er,and a differential joint voting algorithm based on integrated learning is obtained.The user learning situation is personalized ana-lyzed,so that the user's learning difference tags are voted and classified according to the data of different users,thus a chieving per-sonalized learning recommendation guidance.Finally,it extractes features from the learning situation data,builts an evaluation sys-tem with the fusion of new voting algorithms,establishes feature portraits and user interest models,and achieves more accurate and effective recommendations,which has certain practical significance for improving personalized learning recommendation guidance.
Keywords：personalized recommendationalgorithm modelmachine learningacademic datauser portrait
Publication Date：2025-07-20
Online Publishing Date：2025-09-25(First online date of this platform, not the publication date of the document)
Pages：8( 1829-1835,1851 )