Personalized Recommendation Using Deep Reinforcement Learning for Educational Content

The implementation of personalized recommendations of educational content contributes to enhancing the efficiency of resource utilization in education. However, during the recommendation process, it is challenging to extract feature vectors of educational content, which can lead to inaccuracies in the recommendation results. To address this issue, a personalized recommendation algorithm using deep reinforcement learning for educational content is proposed. First, basic user data and educational content resource data are collected, user preference is calculated, multiple features of the educational content are extracted, and feature vectors are constructed. Second, under the influence of deep reinforcement learning, user preference is defined as the state space, and educational content feature vectors are defined as the action space. State value functions and action value functions are computed separately, followed by the calculation of state transition probabilities, the setting of a reward function, and iterative updates based on the reward values generated from user selection of educational content, which then output preliminary recommendation results. Finally, considering the proportion of personalized recommendations, a personalized adjustment function is designed to output accurate personalized educational content recommendation results. Experiments conducted on the Coursera-Course dataset demonstrate that, compared to traditional methods, the proposed method yields an average recall of 96%, and an average sparsity of 22.36%, effectively providing users with accurate personalized educational content recommendations.