Personalized Ideological and Political Course Recommendation using Deep Reinforcement Learning

The political and ideological course resource recommendation is critical in enhancing the efficiency of university and college education. Nonetheless, existing recommendation mechanisms, including collaborative filtering and content-based methods, are plagued by issues such as data sparsity, cold-start issues, and lack of adaptability to the changing learning interests of students. These problems result in low-quality and less personalized recommendations, decreasing student participation and learning achievements. To overcome these difficulties, this research recommends a deep reinforcement learning-based recommendation model, employing the Q-learning algorithm. In contrast to traditional methods, the recommended model learns from students' past interactions and feedback constantly to dynamically adjust course recommendations. By applying a reward-based mechanism, the system guarantees adaptive learning activities and assigns students with suitable ideological and political course materials that align with their interests and academic requirements. Massive experiments on actual educational datasets prove that the designed model far exceeds conventional recommendation methods in precision, relevance, and user satisfaction. Experimental results indicate enhanced recommendation accuracy Experimental results indicate enhanced recommendation accuracy of 0.993, precision of 0.989, recall of 0.978 and F1-score of 0.983, ultimately creating a better learning environment. This work makes a contribution to the field of intelligent education by promoting greater personalization of ideological and political course suggestions. Hybrid reinforcement learning strategies and multi-agent cooperation can be further investigated in future research to enhance recommendation effectiveness and scalability.