Matching Researchers to Funding Calls: A Reproducible Institution-Level Framework

Abstract

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 recommendations. A case study applied to 3,013 researchers from the University of Granada and 291 Horizon Europe topics illustrates the framework in practice. Ultimately, it produces ranked shortlists rather than decisions, and is designed so that each research office can adapt it to its own criteria and use it in line with the principles of responsible metrics.