Skill matching at scale: freelancer-project alignment for efficient multilingual candidate retrieval
<|reference_start|>Skill matching at scale: freelancer-project alignment for efficient multilingual candidate retrieval: Finding the perfect match between a job proposal and a set of freelancers is not an easy task to perform at scale, especially in multiple languages. In this paper, we propose a novel neural retriever architecture that tackles this problem in a multilingual setting. Our method encodes project descriptions and freelancer profiles by leveraging pre-trained multilingual language models. The latter are used as backbone for a custom transformer architecture that aims to keep the structure of the profiles and project. This model is trained with a contrastive loss on historical data. Thanks to several experiments, we show that this approach effectively captures skill matching similarity and facilitates efficient matching, outperforming traditional methods.<|reference_end|>
@article{jouanneau2024skill,
    title={Skill matching at scale: freelancer-project alignment for efficient
  multilingual candidate retrieval},
    author={Warren Jouanneau, Marc Palyart and Emma Jouffroy},
    journal={arXiv preprint arXiv:2409.12097},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.12097},
    primaryClass={cs.CL cs.IR cs.LG cs.SI}
}