SkillMatch: Evaluating Self-supervised Learning of Skill Relatedness

• URL: http://arxiv.org/abs/2410.05006v1
• Date: Mon, 7 Oct 2024 13:05:26 GMT
• Title: SkillMatch: Evaluating Self-supervised Learning of Skill Relatedness
• Authors: Jens-Joris Decorte, Jeroen Van Hautte, Thomas Demeester, Chris Develder,
• Abstract summary: We release SkillMatch, a benchmark for the task of skill relatedness based on expert knowledge mining from millions of job ads. We also propose a scalable self-supervised learning technique to adapt a Sentence-BERT model based on skill co-occurrence in job ads.
• Score: 11.083396379885478
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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