Big data

Algorithm

Process (computing)

Statistics

Convergence (economics)

Operating system

Rounding

Economics

Data mining

Computer science

Mathematical optimization

Political education

Operations research

Collaborative filtering

Analytic hierarchy process

Preference

Mathematics

Politics

Political science

Ideology

Artificial intelligence

Economic growth

Recommender system

Machine learning

Scientific Programming

Aiming to solve the problem that ideological and political education courses in universities are not targeted enough and cannot form personalized recommendations, this paper proposes an ideological and political education recommendation system based on analytic hierarchy process (AHP) and improved collaborative filtering algorithm. Firstly, considering the time effect of student scoring, the recommendation model is transformed into Markov decision process. Then, by combining the collaborative filtering algorithm with reinforcing learning rewards and punishments, an optimization model of student scoring based on timestamp information is constructed. To quantify the degree of students' preference for courses, the analytic hierarchy process is used to convert the students' behavior data into the preference value of courses. To solve the problem of data scarcity, the missing values are predicted by the prediction score rounding filling and the optimization boundary completion method. Experimental results show that the feasibility of the proposed system is verified, and the system has vital accuracy and convergence performance. The ideological and political education recommendation system proposed in this paper has important reference significance for promoting ideological and political education in the era of big data.

Ideological and Political Education Recommendation System Based on AHP and Improved Collaborative Filtering Algorithm