Accurately modeling the relationships between skills is a crucial part of human resources processes such as recruitment and employee development. Yet, no benchmarks exist to evaluate such methods directly. We construct and release SkillMatch, a benchmark for the task of skill relatedness, based on expert knowledge mining from millions of job ads. Additionally, we propose a scalable self-supervised learning technique to adapt a Sentence-BERT model based on skill co-occurrence in job ads. This new method greatly surpasses traditional models for skill relatedness as measured on SkillMatch. By releasing SkillMatch publicly, we aim to contribute a foundation for research towards increased accuracy and transparency of skill-based recommendation systems.

翻译：准确建模技能之间的关系是招聘和员工发展等人力资源流程的关键环节。然而，目前尚无直接评估此类方法的基准。我们基于从数百万招聘广告中挖掘的专家知识，构建并发布了用于技能关联性任务的基准数据集SkillMatch。此外，我们提出了一种可扩展的自监督学习技术，通过招聘广告中技能的共现关系来适配Sentence-BERT模型。在SkillMatch上的评估表明，这一新方法显著超越了传统的技能关联性模型。通过公开SkillMatch，我们旨在为提升基于技能的推荐系统的准确性与透明度提供研究基础。