大型语言模型在MOOC课程推荐中的表现如何？
How Good Are Large Language Models for Course Recommendation in MOOCs?
AI总结：
本文研究将大型语言模型用于MOOC课程推荐，通过提示和微调等多种方法与传统模型对比，实验证明LLMs在准确性、多样性和新颖性上表现良好，展示了其在教育推荐中的潜力。
摘要：
大型语言模型（LLMs）在自然语言处理领域取得了显著进展，并越来越多地被集成到推荐系统中。然而，它们在教育推荐系统中的潜力尚未得到充分探索。本文研究了将LLMs用作通用推荐模型的方法，利用其从大规模语料库中获取的丰富知识来完成课程推荐任务。我们探索了多种方法，从基于提示的方法到更先进的微调技术，并将其性能与传统推荐模型进行了比较。在一个真实的MOOC数据集上进行了大量实验，从准确性、多样性和新颖性等关键维度评估了将LLMs用作课程推荐系统的效果。我们的结果表明，LLMs能够取得与传统模型相当的良好性能，凸显了它们在增强教育推荐系统方面的潜力。这些发现为进一步探索和开发基于LLM的教育推荐方法铺平了道路。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
