How Good Are Large Language Models for Course Recommendation in MOOCs?
It addresses the problem of educational recommendation for MOOC users, but the approach is incremental as it applies existing LLM methods to a new domain.
This paper investigated the use of Large Language Models (LLMs) for course recommendation in MOOCs, finding that LLMs can achieve performance comparable to traditional models.
Large Language Models (LLMs) have made significant strides in natural language processing and are increasingly being integrated into recommendation systems. However, their potential in educational recommendation systems has yet to be fully explored. This paper investigates the use of LLMs as a general-purpose recommendation model, leveraging their vast knowledge derived from large-scale corpora for course recommendation tasks. We explore a variety of approaches, ranging from prompt-based methods to more advanced fine-tuning techniques, and compare their performance against traditional recommendation models. Extensive experiments were conducted on a real-world MOOC dataset, evaluating using LLMs as course recommendation systems across key dimensions such as accuracy, diversity, and novelty. Our results demonstrate that LLMs can achieve good performance comparable to traditional models, highlighting their potential to enhance educational recommendation systems. These findings pave the way for further exploration and development of LLM-based approaches in the context of educational recommendations.