Optimization of Learning Resource Recommendation Algorithm in Intelligent Educational Environments

With the continuous development of intelligent educational environments, personalized recommendation of learning resources has become an important means to improve educational quality and learning outcomes. This paper proposes a hybrid recommendation algorithm based on deep learning-VAE-GAN, aimed at optimizing the automatic recommendation of learning resources in intelligent educational environments. By incorporating the advantages of VAE and GAN, the model is capable of learning deep features of user interests in the latent space, while adversarial training enhances the quality of generated resources. Experimental results show that the VAE-GAN model outperforms traditional collaborative filtering algorithms (UBCF, IBCF and RMF) in learning resource recommendation tasks, achieving better results in evaluation metrics such as precision, recall, and F1-score. Specifically, the VAE-GAN model improves precision@5 and recall@5 by 0.057 and 0.041, respectively, and demonstrates a significant advantage in F1-score@5, indicating its strong practicality in recommendation systems. However, the paper also points out that the VAE-GAN model still faces challenges such as data sparsity, cold start issues, and high computational complexity. The proposed VAE-GAN-based learning resource recommendation algorithm offers new insights for personalized recommendation in intelligent educational environments, with promising practical applications, but further optimization and improvement are needed to meet the demands of a wider range of educational scenarios.