Summary of Skill Matching at Scale: Freelancer-project Alignment For Efficient Multilingual Candidate Retrieval, by Warren Jouanneau et al.
Skill matching at scale: freelancer-project alignment for efficient multilingual candidate retrieval
by Warren Jouanneau, Marc Palyart, Emma Jouffroy
First submitted to arxiv on: 18 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Information Retrieval (cs.IR); Machine Learning (cs.LG); Social and Information Networks (cs.SI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel neural retriever architecture for finding the perfect match between job proposals and freelancers in multiple languages at scale. The method encodes project descriptions and freelancer profiles using pre-trained multilingual language models as the backbone for a custom transformer architecture, trained with a contrastive loss on historical data. Experimental results demonstrate that this approach effectively captures skill matching similarity, outperforming traditional methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps match job proposals to freelancers in many languages. It uses special neural networks and pre-trained language models to do this quickly and well. The idea is to find the right freelancer for a job by looking at what skills they have and what the job needs. This method works better than old ways of doing things. |
Keywords
» Artificial intelligence » Contrastive loss » Transformer
