- Recommender system in a non-stationary context: recommending job ads in
pandemic times, by Guillaume Bied, Solal Nathan, Elia Perennes, Victor
Alfonso Naya, Philippe Caillou, Bruno
Gaillac and Michèle
Sebag, presented at ECML (Poster), 2022.
- On the impact of overfitting in learning to rank using a margin loss: a case
study in job recommender systems by Guillaume Bied and
Solal Nathan, poster presented at
Baylearn and JDSE the same year (Poster), 2022.