SkillMatch: Evaluating Self-supervised Learning of Skill Relatedness
<|reference_start|>SkillMatch: Evaluating Self-supervised Learning of Skill Relatedness: 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.<|reference_end|>
@article{decorte2024skillmatch:,
    title={SkillMatch: Evaluating Self-supervised Learning of Skill Relatedness},
    author={Jens-Joris Decorte, Jeroen Van Hautte, Thomas Demeester and Chris
  Develder},
    journal={arXiv preprint arXiv:2410.05006},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.05006},
    primaryClass={cs.CL}
}