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arXiv · 2504.07108

OKRA: an Explainable, Heterogeneous, Multi-Stakeholder Job Recommender System

Roan Schellingerhout·Francesco Barile·Nava Tintarev
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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.

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Roan Schellingerhout, Francesco Barile, Nava Tintarev. 2025-03-17. OKRA: an Explainable, Heterogeneous, Multi-Stakeholder Job Recommender System. https://arxiv.org/abs/2504.07108

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