用大模型推荐慕课课程，效果不输传统方法。
How Good Are Large Language Models for Course Recommendation in MOOCs?
- 用提示词和微调让大模型理解用户偏好
- 在真实慕课数据上表现接近传统推荐系统
- 适合教育推荐领域研究者参考
大型语言模型（LLMs）在自然语言处理中取得显著进展，并被逐步引入推荐系统。然而其在教育推荐系统中的潜力尚未充分探索。本文研究将LLM作为通用推荐模型，利用其从大规模语料中学习的丰富知识进行课程推荐。我们尝试了从提示工程到高级微调等多种方法，并与传统推荐模型进行对比。在真实世界慕课数据集上进行了大量实验，评估了以LLM为推荐系统的准确性、多样性与新颖性等关键维度。结果表明，LLM能达到与传统模型相当的良好性能，凸显其在教育推荐系统中的应用潜力。这些发现为后续基于LLM的教育推荐方法探索提供了基础。
原文摘要 · Abstract (English)
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.
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