将研究人员匹配到资金申请：一个可重复的机构级框架
Matching Researchers to Funding Calls: A Reproducible Institution-Level Framework
AI总结：
本文提出一个可重复的机构级框架，结合文献计量分析与语义匹配，为研究人员匹配资金机会，通过多组出版物集合和词嵌入比较，生成可操作的推荐。
摘要：
资助推荐系统仍然是学术推荐系统中最少被探索的领域之一，现有方案通常与特定资助机构或学科领域相关。本文提出一个机构级可重复的框架，通过结合文献计量分析与语义匹配，将研究人员匹配到资金机会。该框架通过定义由文献计量标准（如作者身份和时间窗口）确定的多个出版物集合，每个集合独立地与资金申请进行词嵌入比较。研究人员内部标准化和百分位排名将余弦相似度分数转化为可操作的推荐。对格拉纳达大学3013名研究人员和291个地平线欧洲主题的案例研究验证了该方法，显示四个指标捕捉了互补的信号。
英文摘要：
Grant recommendation systems remain one of the least explored areas within academic recommender systems, and existing proposals are typically tied to specific funding agencies or disciplinary domains. This paper presents an institution-level reproducible framework for matching researchers to funding opportunities by combining bibliometric profiling with semantic matching. Rather than representing each researcher through a single aggregated profile, the framework constructs multiple publication sets defined by bibliometric criteria such as authorship position and time window, each independently compared against funding calls using word embeddings. Within-researcher normalisation and percentile-based ranking transform cosine similarity scores into actionable recommendations. A case study applied to 3,013 researchers from the University of Granada and 291 Horizon Europe topics verify it and shows that the four indicators capture complementary signals.
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