比较6种检索方法，找最适合匹配研究生研究兴趣的导师。
Comparing Retrieval Methods for Academic Advisor Discovery: A Six-Method Study of 768 CS Faculty Profiles Across 9 US Universities
- 用六种方法从768位教授中按兴趣匹配导师，涵盖词频、语义和混合策略。
- 重排序模型在10个结果内平均准确率最高（0.477），远超传统词频法。
- 发现简历内容比研究标签更重要，论文摘要拼接反而降低效果。
我们对六种信息检索方法在学术导师发现任务中的表现进行了对比评估：根据研究生的研究兴趣陈述，对计算机科学教师进行相关性排序。方法包括稀疏词法匹配（杰卡德重叠、TF-IDF、BM25）、密集语义检索（all-MiniLM-L6-v2句向量）、混合得分融合及学习排序。评估基于新构建的领域专用数据集：从9所美国计算机系收集的768名教师资料，包含5个查询下的162条相关性标注（0/1/2分）。在全部5个查询中，Reranked 方法取得最高均值 NDCG@10（0.477，标准差0.138），其次为 Semantic（0.450）、Hybrid（0.421）、BM25（0.406）、Jaccard（0.303）和 TF-IDF（0.246）。经邦弗朗尼校正后，仅 TF-IDF 显著劣于其他四种方法；其余对比均不显著。领域消融实验显示，仅使用个人简介时的 NDCG 达 0.634，高于结合研究标签的全模型（0.593）。控制实验表明，拼接 arXiv 论文摘要会使 NDCG@10 下降 0.176，据此提出晚期融合架构。所有代码、爬虫与标注数据均已开源。
原文摘要 · Abstract (English)
We present a comparative evaluation of six information retrieval methods for the task of academic advisor discovery: ranking CS faculty members by relevance to a graduate applicant's research interest statement. The methods span sparse lexical matching (Jaccard overlap, TF-IDF, BM25), dense semantic retrieval (all-MiniLM-L6-v2 sentence embeddings), hybrid score fusion, and learning-to-rank. Evaluation uses a new domain-specific collection: 768 faculty profiles scraped from 9 US CS departments, with 162 graded relevance judgments (grade 0/1/2) across 5 queries representing distinct graduate student research profiles. Across all five queries, Reranked achieves the highest mean NDCG@10 (0.477, std 0.138), followed by Semantic (0.450), Hybrid (0.421), BM25 (0.406), Jaccard (0.303), and TF-IDF (0.246). After Bonferroni correction across all 15 pairwise comparisons, TF-IDF is significantly worse than BM25, Semantic, Hybrid, and Reranked; no other pairwise difference survives correction at 5 queries. A field ablation reveals that biography alone (NDCG 0.634) outperforms the full model combining biography with research area tags (0.593). A controlled experiment shows that concatenating arXiv paper abstracts reduces NDCG@10 by 0.176, motivating a late-fusion architecture. All code, scrapers, and relevance labels are released openly.
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