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

TL;DR

This paper introduces a reproducible framework for matching researchers to funding calls at the institution level, combining bibliometric profiles with semantic matching to improve recommendation accuracy.

Contribution

It presents a novel, reproducible approach that constructs multiple publication-based profiles per researcher and uses semantic similarity for funding call matching.

Findings

The framework effectively captures complementary signals from four indicators.

Case study with 3,013 researchers and 291 funding topics demonstrates the method's validity.

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 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.