Design of an algorithm for recommending elective courses based on collaborative filtering.

At present, college students generally choose courses according to their own interests or understanding of the course, which has a certain subjectivity and blindness. In many cases, students know little about the courses before class, and only rely on the course name to guess the course content, so as to decide whether to take this course. However, the existing studies are mainly aiming at online learning resources which are heterogeneous, these methods cannot be effectively applied to the recommendation of university courses. This paper explores improve collaborative filtering for university application environments, provides a knowledge recommendation algorithm for university elective courses. First, we created individual models of the course and the students based on background information. Next, we use context-based recommendation and "Parent Class Filling" method to reduce the impact of Cold Start and Sparsity problem on the initial stage of the system. Then, recommendations are generated based on the course evaluation model and similarity matrix. We select several commonly used algorithms to achieve the recommendation, and the experimental results proved that the proposed algorithm is accurate and effective. [ABSTRACT FROM AUTHOR]