Construction of Personalized Learning Platform for Students Based on Collaborative Filtering Algorithm

In the context of the rapid development of educational informatization, personalized learning has emerged as a pivotal strategy for elevating teaching quality and learning efficiency. However, the traditional education model overlooks the individual variances among students, consequently resulting in significant differences in learning outcomes. To address this, this paper endeavors to establish a personalized learning platform for students, leveraging collaborative filtering algorithms. The platform comprehensively collects data on students’ learning behaviors and interest preferences and employs advanced collaborative filtering algorithms to offer intelligent recommendations. Subsequently, a performance test is conducted on the platform. The experimental results reveal that the platform’s functions can operate seamlessly, with the accuracy of each indicator exceeding 90% and remaining relatively stable. This suggests that the platform is highly effective in significantly enhancing students’ learning efficiency, thereby effectively validating the practicality of collaborative filtering algorithms in the domain of personalized learning.