Intelligent Recommendation Algorithm for Online Learning Resources Based on Recommendation System

In this paper, how to recommend personalized learning resources to users efficiently and accurately is a key issue. This paper presents a new method, Matrix Deep Q-Factorization (MDQF), which combines Matrix Factorization (MF) and Deep Q-Network (DQN). MDQF method realizes effective decomposition of user-resource interaction matrix through MF to capture the potential interests of users and the characteristics of resources. Simultaneously, DQN is employed for dynamic decision-making in order to refine the recommendation strategy, thereby guaranteeing high recommendation accuracy and enhancing the system's real-time adaptability and robustness. In particular, MF can uncover the latent features of users and resources, boosting the level of personalization in recommendations. DQN, on the other hand, adjusts recommendation strategies according to user feedback through reinforcement learning mechanism to ensure the timeliness and accuracy of recommendation results. Finally, through the comparison experiment and analysis of the proposed framework with the traditional recommendation algorithm, the experimental results show that the MDQF method has significant advantages in improving the recommendation accuracy, user satisfaction and system robustness. Especially in the large-scale online learning environment, MDQF method can more accurately recommend learning resources that meet user needs and improve user experience. This proves the feasibility and effectiveness of this framework in online learning resource intelligent recommendation algorithm.