OKRA为求职与招聘方提供可解释的精准推荐，兼顾公平性。
OKRA: an Explainable, Heterogeneous, Multi-Stakeholder Job Recommender System
- 基于图神经网络和注意力机制，支持多主体可解释推荐。
- 在两个数据集上nDCG显著优于六个基线模型。
- 发现现有模型倾向城市候选人与岗位，OKRA更公平。
推荐系统在招聘领域被列为‘高风险’，需满足严格的可解释性与公平性要求。为实现不同参与方的个性化解释，并处理高度异构的招聘数据，我们提出一种基于图神经网络的可解释多利益相关者职位推荐系统——职业知识注意力推荐器（OKRA）。该方法能同时为求职者和企业提供推荐及解释。实验表明，OKRA在两个数据集上的nDCG指标显著优于六个基线模型。此外，测试发现现有模型对城市地区候选人和职位存在偏向。整体结果表明，OKRA在准确性、可解释性与公平性之间取得良好平衡。
原文摘要 · Abstract (English)
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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