Skills2Job: A recommender system that encodes job offer embeddings on graph databases

Abstract We propose a recommender system that, starting from a set of users’ skills, identifies the most suitable jobs as they emerge from a large dataset of Online Job Vacancies (OJVs). To this aim, we process 2.5M+ OJVs posted in three different countries (United Kingdom, France, and Germany), training several embeddings and performing an intrinsic evaluation of their quality. Besides, we compute a measure of skill importance for each occupation in each country, the Revealed Comparative Advantage (rca). The best vector model, one for each country, together with the rca, is used to feed a graph database, which will serve as the keystone for the recommender system. Results are evaluated through a user study of 10 labor market experts, using P@3 and nDCG as scores. Results show a high precision for the recommendations provided by skills2job , and the high values of nDCG (0.985 and 0.984 in a [0,1] range) indicate a strong correlation between the experts’ scores and the rankings generated by skills2job .