Workshop on Recommender Systems for Human Resources

The field of Human Resources (HR) is at the forefront of adopting AI technologies. According to PWC, over 40% of HR-functions of international companies use AI-applications. This so-called HR Technology (HR Tech) aims to replace or support Human Resource functions such as talent acquisition and management, employee compensation, workforce analytics, and performance management. Recommender Systems, broadly defined as systems that aim to support users in decision making by suggesting and offering relevant content, play an integral role in the rapid rise of HR Tech. Their applications range from assisting the talent acquisition process through matching, analyzing resumes or other user representations for candidate screening and automated assessment, to broader tasks such as recommendations for upskilling. Alongside their growing use within the HR space, the European Commission has labeled such systems as “high-risk”, as automation here can directly impact the (working) lives of people. In this light, the rise of AI-assisted hiring and screening is met with caution, and is a widely-used example application area in AI ethics and fairness literature. The purpose of this workshop is to provide a central forum where researchers and practitioners alike can come together to study and discuss the domain-specific aspects, challenges, and opportunities of recommender systems within HR applications.

  • Toine Bogers – Aalborg University Copenhagen, Denmark
  • David Graus – Randstad Groep Nederland, the Netherlands
  • Francisco Gutiérrez – KU Leuven, Belgium
  • Chris Johnson – Indeed, US
  • Mesut Kaya – Aalborg University Copenhagen, Denmark
  • Sepideh Mesbah – Randstad Groep Nederland, the Netherlands


Full day.

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