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

2026-04-07

AI Summary

This study addresses the limited generalizability and reproducibility of existing research funding recommendation systems, which are often confined to specific institutions or disciplines. To overcome this, the authors propose an institution-level reproducible framework that constructs multidimensional bibliometric subsets—rather than a single aggregated profile—and integrates word embeddings with semantic matching to deliver precise funding opportunity recommendations. The method generates multiple publication sets based on author affiliation and temporal windows, computes cosine similarities between each set and funding topics, and then fuses four complementary metrics through intra-group normalization and percentile ranking. Evaluated on 3,013 researchers from the University of Granada and 291 Horizon Europe topics, the framework demonstrates its capacity to capture complementary signals effectively, significantly enhancing the granularity and adaptability of funding recommendations.

Technology Category

Data Mining & Knowledge Management: Recommender SystemsApplication Domains: Humanities & Computational Social ScienceSearch and Optimization: Metareasoning and Metaheuristics

Application Category

User Modeling, Personalization and Recommendation: Federated recommendation systems and personalizationSearch and Retrieval-Augmented AI: Web evaluation methodologies and metricsGraph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

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.

Problem

• grant recommendation
• researcher matching
• funding calls
• institution-level framework
• academic recommender systems

Innovation

• bibliometric profiling
• semantic matching
• word embeddings
• institution-level framework
• grant recommendation