Find my next job: labor market recommendations using administrative big data

Abstract

Labor markets are undergoing change due to factors such as automatization and globalization, motivating the development of occupational recommender systems for jobseekers and caseworkers. This study generates occupational recommendations by utilizing a novel data set consisting of administrative records covering the entire Danish workforce. Based on actual labor market behavior in the period 2012-2015, how well can different models predict each users' next occupation in 2016? Through offline experiments, the study finds that gradient-boosted decision tree models provide the best recommendations for future occupations in terms of mean reciprocal ranking and recall. Further, gradient-boosted decision tree models offer distinct advantages in the labor market domain due to their interpretability and ability to harness additional background information on workers. However, the study raises concerns regarding trade-offs between model accuracy and ethical issues, including privacy and the social reinforcement of gender divides.