Design and Application of English Course Learning System Based on Hybrid Recommendation Algorithm

Intelligent English course learning system utilizing an advanced hybrid recommendation algorithm to optimize personalized learning experiences. The research addresses a critical limitation of traditional recommendation systems: their inability to effectively accommodate diverse learner preferences and dynamically evolving content. To overcome these challenges, the proposed system integrates neural matrix factorization (NeuMF) for deep feature learning and knowledge graph embeddings for semantic relationship modelling. The hybrid architecture employs an adaptive weighting mechanism that automatically balances these approaches based on real-time performance metrics. For thorough assessment, the system was evaluated using the “Duolingo English Learning Dataset”, which involves more than 500,000 learner interactions. Experimental outcomes show impressive enhancements over baseline approaches, with 95.8% precision, 95.9 % recall, and an impressive 95.8 % F1-score. The uniqueness of the system is that it has the ability to self-optimize dynamically, through which ongoing analysis of learner feedback (such as engagement time, quiz performance, and content ratings) allows for real-time refinement of recommendations. Other measures indicate 15 % improved student retention and 20 % quicker skill uptake over baseline systems. This work adds both a practical model to instruction technology and theoretical depth to understanding the optimization of hybrid recommendation systems for dynamic, sophisticated learning environments.