A Personalized Recommendation Algorithm for Political and Ideological Courses in Colleges Using Multiple Interests of Users

Abstract

Due to the development of social economy and the continuous growth of science and technology, the historical progress of the information age has opened up. With its mass audience, vast range, and powerful guiding technology, streaming media technology has significantly reduced the time and gap among people in various parts of the world, particularly in the field of education. However, political and ideological education classes in colleges are not sufficiently focused, and as a result, personalized recommendations and analysis of users' various interests are not possible because of low precision, low user coverage, excessive recommendation time, and poor optimal solution capability. To fill these issues, this study provides a personalized recommendation system for political and ideological courses in colleges based on the interests of multiple users. This paper developed a framework for mining knowledge points in political and ideological course, mined knowledge points in a course using association rules, and calculated the weight of knowledge points using hierarchical tree theory. Apart from these, this paper established the user interest model to obtain the learning characteristics of learners. The experimental results show that the proposed algorithm has higher recommendation accuracy, higher user coverage, short recommendation time, and good optimization ability, which proved that it has a high application value. Based on this study, it can be argued that in modern era of education, the proposed recommendation system of political and ideological education has significant comparative importance for promoting political and ideological education in colleges.