Find My Next Job: Labor Market Recommendations Using Administrative Big Data

Snorre Sylvester Frid-Nielsen

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review


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.
Original languageEnglish
Title of host publicationRecSys '19: Proceedings of the 13th ACM Conference on Recommender Systems
PublisherAssociation for Computing Machinery
Publication date2019
ISBN (Print)9781450362436
Publication statusPublished - 2019
Event13th ACM Conference on Recommender Systems - København, Denmark
Duration: 16 Sep 201920 Sep 2019
Conference number: 13


Conference13th ACM Conference on Recommender Systems
Internet address

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