Personalised E-Learning Course Recommendation System

his study presents a Hybrid NLP-based personalised e-learning recommendation system that improves course suggestion accuracy and relevance. By integrating content-based and collaborative filtering with TF-IDF and cosine similarity, the model achieved higher accuracy, precision, recall, and F1-score than traditional methods. It effectively reduces information overload and addresses overspecialization and cold start issues. The system remains computationally efficient, interpretable, and scalable, making it suitable for academic use. Compared to deep learning approaches, it offers a lightweight and practical alternative. Overall, the framework enhances learner engagement and supports structured, data-driven academic decision-making in digital education environments.

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