Research on Online Course Recommendation Model Based on Improved Collaborative Filtering Algorithm
Lianfen Zhao, Zhengjun Pan
Abstract
Aiming at the problem of sparse data and poor recommendation effect in online course recommendation, this paper proposes an improved online course intelligent recommendation model based on user implicit behavior collaborative filtering. Through data analysis, the implicit behavior data such as user login details, learning details and course selection details are mined, and the online course intelligent recommendation is carried out combined with the collaborative filtering algorithm based on items. Based on the operation data of an education platform in recent two years, the experimental results show that the precision and recall rate of the improved collaborative filtering recommendation model are improved under different K values.