OKRA: an Explainable, Heterogeneous, Multi-Stakeholder Job Recommender System
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866913787196473344 |
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| author | Schellingerhout, Roan Barile, Francesco Tintarev, Nava |
| author_facet | Schellingerhout, Roan Barile, Francesco Tintarev, Nava |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_07108 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | OKRA: an Explainable, Heterogeneous, Multi-Stakeholder Job Recommender System Schellingerhout, Roan Barile, Francesco Tintarev, Nava Information Retrieval Artificial Intelligence Human-Computer Interaction 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. |
| title | OKRA: an Explainable, Heterogeneous, Multi-Stakeholder Job Recommender System |
| topic | Information Retrieval Artificial Intelligence Human-Computer Interaction |
| url | https://arxiv.org/abs/2504.07108 |