Enhancing online education recommendations through clustering-driven deep learning

The rapid growth of online education platforms has necessitated the development of sophisticated recommendation systems to help learners navigate the vast array of available courses. Traditional recommendation techniques such as collaborative filtering, content-based filtering, and matrix factorization, while useful, face significant challenges including data sparsity, cold start problems, and the need for extensive feature extraction. This research proposes a novel clustering-driven deep learning model designed to address these limitations and enhance the accuracy and personalization of course recommendations. By integrating clustering techniques with Bidirectional Long Short-Term Memory (BiLSTM) networks and Multi-Layer Perceptrons (MLP), the model effectively groups similar courses and users, mitigating data sparsity and cold start issues. The use of BiLSTM enhances feature extraction from course descriptions, leading to more precise content-based recommendations. The model's combined approach ensures both content-based and collaborative filtering aspects are considered, resulting in highly personalized suggestions. Evaluation results demonstrate that the proposed model significantly improves recommendation 96 % of accuracy and scalability compared to existing methods. This study's contributions offer a robust framework for advancing recommendation systems in online education, ultimately enhancing user engagement and satisfaction.