Optimization of Personalized Learning Resource Recommendation Using SVD Algorithm in Student Management

Personalized learning resource recommendation optimization in student management is an important task aimed at providing students with the most suitable learning resources based on their interests and ability characteristics. This article investigated the application of SVD (Singular Value Decomposition) algorithm. The SVD algorithm decomposed students' historical learning data to obtain their potential interests and ability characteristics. Then, by comparing the characteristics of students with the characteristics of various learning resources, personalized learning resources suitable for students were recommended. The experiment in the article found that coverage, diversity, and novelty were significantly improved, with the highest values of 95.6%, 94.9%, and 98%, respectively. By implementing the comprehensive application of SVD algorithm and evaluation indicators, personalized learning resource recommendation optimization in student management can better meet the learning needs of students, and improve learning effectiveness and user satisfaction.