Course Selection Recommend System Based on Collaborative Filtering Algorithm

Abstract

The persistent advancements in technologies have resulted in the birth of the Information Age. However, with the abundance of resources and the vast amount of information available on the Internet, users have difficulty in filtering redundant information and finding information that they really need. Therefore, to solve these problems, search engines and recommendation systems were created to help users find the information they really need. Recommendation systems can recommend personalized resources to users according to their preferences. In most cases, users cannot explicitly articulate their real needs, but their potential needs can be explored through the recommendation system. Therefore, research on recommendation systems is very important. In teaching activities, the course selection system is very essential. Course selection system not only allows students to clearly understand the focus of a particular course, but also allows them to make their own choices and develop their own direction, Therefore, the course selection system should recommend the courses that students are really interested in, so that each student can choose the courses that they really need. Accordingly, this thesis designs and implements a personalized course selection recommendation system based on the collaborative filtering recommendation algorithm. The architecture of the system adopts the mainstream Spring MVC+Spring + Mybatis framework technology in the industry. The system can infer students’ course selection preferences based on their course selection records and recommend courses that students may be interested in based on the collaborative filtering algorithm. At the same time, students can evaluate the course after taking it, which can improve the recommendation accuracy of the system. Therefore, this course recommendation system provides a good solution for personalized teaching resource recommendations.