We have two papers accepted at “The First International Workshop on Computational Jobs Marketplace“, co-located with WSDM 2022. Both papers are based on work done by two of our former thesis interns at Randstad Groep Nederland!

• N. Vermeer, V. Provatorova, D. Graus, T. Rajapakse, and S. Mesbah, “Using robbert and extreme multi-label classification to extract implicit and explicit skills from dutch job descriptions,” in Compjobs ’22: the first international workshop on computational jobs marketplace, 2022.
  [Bibtex]

  @inproceedings{vermeer2022using,
  author = {Vermeer, Ninande and Provatorova, Vera and Graus, David and Rajapakse, Thilina and Mesbah, Sepideh},
  title = {Using RobBERT and eXtreme Multi-Label Classification to Extract Implicit and Explicit Skills From Dutch Job Descriptions},
  year = {2022},
  booktitle = {CompJobs '22: The First International Workshop on Computational Jobs Marketplace},
  numpages = {5},
  location = {Online},
  month={2}
  }

☝️ Ninande Vermeer worked under supervision of Sepideh Mesbah and Vera Provatorova (UvA) on: “Using RobBERT and eXtreme Multi-Label Classification to Extract Implicit and Explicit Skills From Dutch Job Descriptions” in which we study to what extent a RobBERT-XMLC model can be used to extract explicit and implicit skills from Dutch job descriptions.

• S. van Els, D. Graus, and E. Beauxis-Aussalet, “Improving fairness assessments with synthetic data: a practical use case with a recommender system for human resources,” in Compjobs ’22: the first international workshop on computational jobs marketplace, 2022.
  [Bibtex]

  @inproceedings{vanels2022improving,
  author = {van Els, Sarah-Jane and Graus, David and Beauxis-Aussalet, Emma},
  title = {Improving Fairness Assessments with Synthetic Data: a Practical Use Case with a Recommender System for Human Resources},
  year = {2022},
  booktitle = {CompJobs '22: The First International Workshop on Computational Jobs Marketplace},
  numpages = {5},
  location = {Online},
  month={2}
  }

✌️ Sarah-Jane van Els worked under supervision of myself and Emma Beauxis-Aussalet (Civic AI Lab) on “Improving Fairness Assessments with Synthetic Data: a Practical Use Case with a Recommender System for Human Resources” in which we explore approaches and methods for assessing algorithmic bias by using synthetic data to improve the size and representativity of a test set used for training candidate recommender systems.

👏 Proud of our former interns for having published their work! And happy with the collaborations we have had with our co-authors 😁.