Design and Research of Students’ Secured Education System Based on Personalized Recommendation

This study aims to develop a secure education solution that meets the individual needs of different college students, helping them grasp knowledge better, enhance their learning enthusiasm, and stimulate their motivation.The research adopts experimental design and analysis methods, setting up experimental and control groups, collecting experimental data, and conducting data analysis.The matrix factorization algorithm is used to obtain a suitable system, and the experimental results show that personalized recommendation systems can recommend secure education resources that meet the users' needs based on their personal characteristics and interests, thereby increasing their attention and interest in learning and enhancing learning effectiveness.The college students' secure education system based on personalized recommendation has potential and room for development, and its performance and user experience can be improved through continuous optimization of recommendation strategies and algorithms.This research provides valuable references for the design and study of personalized recommendation systems in college students' secure education.