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.

Read the full study: "Combined Unsupervised and Contrastive Learning for Multilingual Job Recommendation"

This article contributes to the broader collection of external ESCO publications, showcasing the use of ESCO within various methodologies or its presentation in both European and International contexts. As ESCO becomes increasingly used in applications and research projects across Europe and beyond, it is valuable to collect such sources and share best practices by diverse stakeholders. Therefore, this collection of external publications strengthens the exchange of knowledge within the ESCO community and can contribute to mutual learning in the field of skills, occupations and qualifications among European and international actors. If you are interested in sharing your publication, please write to EMPL-ESCO-SECRETARIAT@ec.europa.eu