BERT-Based Personalized Course Recommendation System from Online Learning Platform

In the realm of online education, the abundance of courses poses a challenge for learners making enrollment decisions. To tackle this, we propose a novel recommendation system utilizing BERT (Bidirectional Encoder Representation from Transformers). By analyzing learners' profiles and past enrollments, personalized recommendations are generated, enhancing engagement. Our study analyzes 3,678-course records from Udemy across four subjects, catering to all proficiency levels. Harnessing BERT's capabilities, the system promises to revolutionize online learning by ensuring optimal course selection. Validation through a comprehensive survey underscores its performance and usability, offering insights for enhancing online education platforms and empowering learners.