OKRA: an Explainable, Heterogeneous, Multi-Stakeholder Job Recommender System
This addresses fairness and explainability requirements in recruitment for candidates and companies, but it is incremental as it builds on existing graph neural network methods.
The authors tackled the problem of high-risk job recommendations by developing OKRA, an explainable multi-stakeholder recommender system using graph neural networks, which performed substantially better than six baselines in terms of nDCG on two datasets.
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.