Graph neural networks vs. Traditional methods for recommending MOOC courses: a comparative analysis (2023)
In recent years, online education has seen remarkable growth, particularly through Massive Open Online Course (MOOC) platforms. These platforms offer a wide range of open courses, making it a problem for users to select one from thousands of courses. Navigating this vast selection led to recommendation systems emerging, enhancing the learning experience by guiding students toward courses aligned with their interests. Addressing this challenge involves three primary approaches. The content-based approach uses the characteristics content of the seen courses to suggest new ones. Collaborative filtering taps into interactions among users with similar preferences, proposing relevant courses. The hybrid approach combines both methods for a comprehensive recommendation system. In a context marked by the rapid expansion of Massive Open Online Courses (MOOCs), there has been a parallel surge in the field of deep learning along with its diverse architectures. Against this backdrop, the focus of this investigation centers on Graph Neural Networks (GNNs), a deep learning architecture explicitly tailored to tackle structured data. In this study, we propose and validate a novel course recommendation model utilizing GNNs on the Xuetang MOOC platform. This research's significance lies in enhancing online course recommendations using GNNs, aiming to elevate user satisfaction and learning effectiveness. Finally, for a comprehensive analysis, we'll compare our model against traditional approaches using the same dataset. This determines GNN's convenience in recommendation precision and relevance against the traditional methods.