Research of online courses recommendation based on deep learning.

This paper delves into leveraging deep learning techniques, such as graph neural networks (GNNs), Transformer, and techniques in Large Language Models (LLMs), to enhance course recommendation systems in e-learning platforms. Recommendation methods have some short-comes in the case of online course with less information and choic less logic. Our research proposes novel algorithms that use graph collaborative filtering and sequential recommendation to improve recommendation accuracy and personalization. By analyzing user behavior patterns and course attributes, our approach aims to provide smarter and more efficient course recommendation services, ultimately enhancing learning outcomes and experiences in e-learning environments. This research not only contributes to the advancement of e-learning technology but also provides valuable insights for the broader application of deep learning in smart education. [ABSTRACT FROM AUTHOR]