用课程内容+知识图谱推荐课，更准更懂学生需求。
RAGEAR: Retrieval-Augmented Graph-Enhanced Academic Recommender
- 结合语义检索与知识图谱，细粒度匹配课程内容。
- 在152个查询上，推荐排名显著优于基线模型。
- 适合需要精准课程推荐的教育AI场景。
我们提出RAGEAR（检索增强型图增强学术推荐系统），一种用于学术课程推荐的神经符号推荐系统。RAGEAR将全课程文本的密集检索与符号化知识图谱相结合，知识图谱建模了课程、章节、文本片段、学分、学习计划和课程结构信息。该图支持基于结构化约束（如学分、学科、学习计划、先修要求）的符号过滤与上下文化。不同于仅依赖元数据的方法，RAGEAR通过检索与学生查询语义对齐的文本片段，利用细粒度教学内容。主要贡献在于一种图感知聚合函数，可将片段级证据传播至课程级推荐。评分综合三个因素：与某课程相关的检索相似性占比、相关片段的排名强度，以及证据在各章节中的分布。我们在152个模拟学生查询上通过人工评估样本和大规模大模型相关性评估进行验证。结果表明，使用课程文本优于仅依赖元数据的检索，且RAGEAR进一步提升排名质量，尤其在高排名推荐中表现突出。
原文摘要 · Abstract (English)
We present RAGEAR (Retrieval-Augmented Graph-Enhanced Academic Recommender), a neurosymbolic recommender system for academic course recommendation. RAGEAR combines dense retrieval over full lecture transcripts with a symbolic Knowledge Graph modelling courses, lessons, transcript chunks, credits, study plans, and curricular information. The Knowledge Graph supports symbolic filtering and contextualisation based on structured constraints, such as credits, academic disciplines, study plans, and prerequisites. Unlike metadata-based approaches, it exploits fine-grained instructional content by retrieving transcript chunks semantically aligned with a student's query. The main contribution is a graph-aware aggregation function that propagates chunk-level evidence to course-level recommendations. The score combines three factors: the share of retrieved similarity associated with a course, the rank-based strength of its relevant chunks, and the distribution of evidence across lessons. We evaluate RAGEAR on 152 student-like queries through a human evaluation sample and a large-scale LLM-based relevance assessment. Results show that lecture transcripts improve over metadata-only retrieval, and that RAGEAR further improves ranking quality over a transcript-based normalized SumP baseline, especially for top-ranked recommendations.
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