用大模型根据自然语言提问推荐课程，帮学生发现感兴趣的新课。
From Interests to Insights: An LLM Approach to Course Recommendations Using Natural Language Queries
- 通过LLM生成理想课程描述，再用嵌入向量匹配真实课程。
- 基于课程描述相似度检索，支持动态变化的课程体系。
- 适合探索兴趣、未定专业的学生和学业顾问使用。
美国多数大学鼓励学生在确定专业前广泛探索学术领域，并通过满足多样化要求获得知识广度。每学期学生需从数千门跨数十个学科的课程中选择，而校园课程环境动态变化，现有信息沟通与搜索功能不足，限制了学生发现新课程的能力。为此，我们提出一种基于大语言模型（LLM）的课程推荐系统，采用检索增强生成（RAG）方法处理课程描述语料库。系统首先根据用户自然语言查询生成一个‘理想’课程描述，再将其转换为嵌入向量，通过对比嵌入相似性查找内容相近的真实课程。我们介绍了该方法，并评估了部分提示词的质量与公平性。最后讨论了在校园部署试点系统的步骤。
原文摘要 · Abstract (English)
Most universities in the United States encourage their students to explore academic areas before declaring a major and to acquire academic breadth by satisfying a variety of requirements. Each term, students must choose among many thousands of offerings, spanning dozens of subject areas, a handful of courses to take. The curricular environment is also dynamic, and poor communication and search functions on campus can limit a student's ability to discover new courses of interest. To support both students and their advisers in such a setting, we explore a novel Large Language Model (LLM) course recommendation system that applies a Retrieval Augmented Generation (RAG) method to the corpus of course descriptions. The system first generates an 'ideal' course description based on the user's query. This description is converted into a search vector using embeddings, which is then used to find actual courses with similar content by comparing embedding similarities. We describe the method and assess the quality and fairness of some example prompts. Steps to deploy a pilot system on campus are discussed.
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