Abstract
The transformative power of artificial intelligence is revolutionizing the talent acquisition domain. Automatic job recommendation systems are emerging as a key component of this transformation. This study presents a new multilingual job recommendation solution that leverages combined unsupervised and contrastive learning to effectively model the semantic similarity between job titles across 11 languages. Our approach pre-trains a multilingual encoder using unsupervised learning on co-occurrence information of skills and job titles, followed by fine-tuning via contrastive learning on a dataset of similar and dissimilar job pairs based on the European Skills, Competences, Qualifications and Occupations (ESCO) taxonomy. This sequential learning strategy significantly enhances representation quality. Our novel multilingual job title encoder achieves strong ranking results across all languages, with 4.3% improvement in mean Average Precision (mAP) for English compared to previous state-of-the-art monolingual solutions. The proposed method also offers very good cross-lingual capabilities, enabling the ranking of jobs in different languages with improved alignment and uniformity properties in the representation space.
