Recommendation Systems in Education

In the dynamic landscape of online education, the quest for effective course discovery is a multifaceted challenge. This paper investigates a recommendation system tailored to enrich course discovery on Udemy, employing a context-based, collaborative, and hybrid approach. Our system excels with a multi-dimensional course recommendation strategy, seamlessly blending three core techniques: context-based recommendations that consider user profiles and learning contexts, collaborative filtering to identify behavioral patterns and hybridization for fine-tuned suggestions aligned with immediate learning needs and user community insights. The paper delves into evaluating recommendation systems using appropriate metrics and benchmark datasets, addressing challenges like limited data and initiation hurdles with practical solutions. Looking forward, we explore the potential of emerging technologies, including deep learning and reinforcement learning, to further enhance recommendation algorithms. Our vision prioritizes personalization, diversity, and transparency, shaping the future of course recommendations on Udemy.