Skill matching at scale: freelancer-project alignment for efficient multilingual candidate retrieval
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929505832009728 |
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| author | Jouanneau, Warren Palyart, Marc Jouffroy, Emma |
| author_facet | Jouanneau, Warren Palyart, Marc Jouffroy, Emma |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_12097 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Skill matching at scale: freelancer-project alignment for efficient multilingual candidate retrieval Jouanneau, Warren Palyart, Marc Jouffroy, Emma Computation and Language Information Retrieval Machine Learning Social and Information Networks 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. |
| title | Skill matching at scale: freelancer-project alignment for efficient multilingual candidate retrieval |
| topic | Computation and Language Information Retrieval Machine Learning Social and Information Networks |
| url | https://arxiv.org/abs/2409.12097 |