Work

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

Year: 2026

Type: preprint

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.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.19247326

Authors: Arroyo-Machado, Wenceslao, Lázaro-Soraluce, Laura, Ortega-Sevilla, Clara, de la Fuente-Gutiérrez, Enrique, Torres-Salinas, Daniel

Institutions: Instituto de Gestión de la Innovación y del Conocimiento, Universidad de Granada

Language: English

Topic: Recommender Systems and Techniques

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7141611225

Cites:

Cited by:

Related to: