Personalized Recommendation Algorithm for University Civics Courses with Multiple User Interests

Abstract

In response to the problems of unity, lack of relevance, lack of synergy, and inability to form a personalized collaborative education mechanism in the current curriculum setting of university thinking and politics education, a personalized recommendation system for university thinking and politics courses based on the multiple interests of users was developed. The system of interests is divided into two parts: first is initial interest guidance, in which the N meta-model is used to learn the context of known course processes; second is user interest extraction; at the end of creating the recommendation process, facing the diversity of user interests, probabilistic latent semantic analysis trains the interest-service-flow distribution of students to recommend the civics course that matches the current interest for students. A good recommendation algorithm can simulate learners’ enthusiasm and give full play to different learners’ learning personalities. The simulation experiments show that the system is stable in operation, complete in function, and has strong practicality and robustness, which is of positive significance in creating a win-win, diverse, and innovative atmosphere for students’ and teachers’ thinking education in colleges and universities.