OKRA: an Explainable, Heterogeneous, Multi-Stakeholder Job Recommender System
Abstract
The use of recommender systems in the recruitment domain has been labeled as 'high-risk' in recent legislation. As a result, strict requirements regarding explainability and fairness have been put in place to ensure proper treatment of all involved stakeholders. To allow for stakeholder-specific explainability, while also handling highly heterogeneous recruitment data, we propose a novel explainable multi-stakeholder job recommender system using graph neural networks: the Occupational Knowledge-based Recommender using Attention (OKRA). The proposed method is capable of providing both candidate- and company-side recommendations and explanations. We find that OKRA performs substantially better than six baselines in terms of nDCG for two datasets. Furthermore, we find that the tested models show a bias toward candidates and vacancies located in urban areas. Overall, our findings suggest that OKRA provides a balance between accuracy, explainability, and fairness.
Cite
@article{arxiv.2504.07108,
title = {OKRA: an Explainable, Heterogeneous, Multi-Stakeholder Job Recommender System},
author = {Roan Schellingerhout and Francesco Barile and Nava Tintarev},
journal= {arXiv preprint arXiv:2504.07108},
year = {2025}
}
Comments
17 pages, 1 figure, 1 table, to be published in the proceedings of ECIR2025