A network embedding approach to a fair, efficient and fulfilling job market.

01 November 2020 → Ongoing
Regional and community funding: Special Research Fund
Research disciplines
  • Natural sciences
    • Machine learning and decision making
network embedding job market fairness machine learning artificial intelligence
Project description

The development of a model, based on Conditional Network Embedding, that represents job seekers, vacancies and metadata without sensitive information, like gender, in low-dimensional embeddings. Downstream tasks that aim for more job market efficiency, like interest prediction between job seekers and vacancies, are consequently fairer. This model can also provide suggestions to job seekers and employers to widen their reach.