Online English teaching resource recommendation method design based on LightGCNCSCM

With the explosive growth of online English teaching resources, how to achieve personalized and high-quality resource recommendations has become a key issue that needs to be urgently solved. Existing methods have significant limitations in aspects such as cold start scenarios, semantic feature fusion, and the balance between computational efficiency and recommendation quality. The research proposes an online English teaching resource recommendation method. The local and global features of the user-resource interaction graph are captured through Lightweight graph convolutional networks, and the resource semantic vectors are extracted in combination with the content-based similarity calculation model. This can synergistically optimize behavior structure and content semantics. Experiment results show that this method significantly improves the recommendation quality in the cold start scenario. It balances the novelty of recommendation results and user preference matching through a dynamic weight allocation mechanism, while maintaining relatively low computational complexity. This method provides an efficient and robust personalized recommendation solution for online education platforms.