Personalized Recommendation Algorithm for Curriculum- and Politics-Oriented Hybrid Teaching Resources

To meet the needs of personalized learning of social talent and students and enhance the effect of hybrid teaching resource recommendations, this paper proposes a personalized recommendation algorithm for hybrid teaching resources oriented to curriculum ideology. Based on the idea of curriculum thinking, the author chooses and acquires mixed teaching resources, judges the difficulty of teaching resources and calculates the similarity of learning resources. The mixed teaching resources are represented by classification, and the knowledge level of students is modeled, analyzed and diagnosed by combining the answer situation matrix and the knowledge point matrix. According to the learning preference of students and the sequence of knowledge points and the difficulty of learning resources, the personalized recommendation of hybrid teaching resources can be realized. Experimental results show that the proposed algorithm has a higher recommendation accuracy, recall andF1 than the minimum value, which fully proves that the proposed algorithm is better.