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!
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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.
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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 😁.

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