Based on Collaborative Filtering Personalized Recommendation for Online Learning
Yiwei Qian, Ying Li, Yongbin Wang, Tao Hu · 2020
Nowadays, Internet technology has flourished and become a hot, infiltrating into all aspects of our lives. The education industry has also progressed with the innovation of technology, and online learning has emerged. Developers of online learning always put their own learning resources on the platform for the students to use. Students can learn by searching for content that they are interested in. But looking up in massive content may waste too much time. Therefore, the system can recommend different learning resources for different students or not through some historical browsing and the learning behavior or other contents becomes a important problem to be solved. This paper is aimed at this issue mainly to study the personalized recommendation of learning platform based on the students' learning behaviors.