Skill matching at scale: freelancer-project alignment for efficient multilingual candidate retrieval
This work addresses the challenge of efficient multilingual candidate retrieval for freelancer platforms, representing an incremental improvement over existing methods.
The paper tackles the problem of matching freelancers to job proposals at scale across multiple languages by proposing a novel neural retriever architecture that encodes project descriptions and freelancer profiles using pre-trained multilingual language models. The method outperforms traditional approaches in capturing skill matching similarity and enabling efficient matching.
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.