Optimizing Recommendation Systems in E-Learning: Synergistic Integration of Lang Chain, GPT Models, and Retrieval Augmented Generation (RAG)

The emergence of recommendation systems stands as an essential response to the increasing complexity of choices, providing personalized suggestions. Recent advances in artificial intelligence, particularly in natural language processing (NLP), enhance the effectiveness of these systems, driven by cutting-edge models such as OpenAI’s GPT-3.5. However, challenges persist, including the “cold start” and hallucination issues within Large Language Models (LLMs). Our research focuses on exploring the use of OpenAI’s embedding to enhance online course recommendations, while integrating LLMs Lang Chain and GPT-3.5 into the E-learning domain. We introduce the “Retrieval Augmented Generation” (RAG) approach to address these challenges. Experiments conducted on a real E-learning dataset evaluate the models’ ability to understand and generate relevant recommendations. The results demonstrate the effectiveness of the contributions, emphasizing the specific opportunities provided by the RAG approach to tackle challenges in recommendation systems based on LLMs.