Personalized Recommendation Method of Ideological and Political Education Resources Based on Data Mining

Abstract

With the continuous development of educational concepts, personalized education has become an important trend in the field of education. In order to achieve better results in ideological and political education in universities, it is very important to carry out personalized recommendations of ideological and political education resources. Conventional personalized recommendation methods for ideological and political education resources mainly use K-Mean clustering algorithm to classify resource levels, which is vulnerable to the dynamic changes of user preferences, resulting in low clicks of resource recommendations. Therefore, a new personalized recommendation method for ideological and political education resources needs to be designed based on data mining. That is to say, it analyzes the characteristics of personalized recommendation users of resources, constructs a personalized recommendation user model of educational resources, and then designs a recommendation collaborative filtering algorithm using data mining, thus realizing personalized recommendation of ideological and political education resources. The experimental results show that the personalized recommendation method of ideological and political education resources designed based on data mining has a good recommendation effect, and the recommended resource samples have a high number of hits, reliability, and certain application value, making a certain contribution to promoting the sharing of ideological and political education resources.