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.

翻译：基金推荐系统一直是学术推荐系统中探索最少的领域之一，现有方案通常局限于特定资助机构或学科领域。本文提出了一种机构级可复现框架，通过结合文献计量分析与语义匹配，将研究人员与基金机会进行匹配。该框架并非通过单一聚合档案表征每位研究人员，而是构建由作者身份定位和时间窗口等文献计量标准定义的多个发表集，并分别使用词嵌入技术将这些发表集与基金项目进行独立比较。通过研究人员内部归一化与基于百分位数的排名转换，将余弦相似度分数转化为可操作的建议。针对格拉纳达大学3013名研究人员和291个地平线欧洲主题的应用案例验证表明，四个指标捕捉到了互补信号。