EvalRS aims to bring together practitioners from industry and academia to foster a debate on rounded evaluation of recommender systems, with a focus on real-world impact across a multitude of deployment scenarios. Recommender systems are often evaluated only through accuracy metrics, which fall short of fully characterizing their generalization capabilities and miss important aspects, such as fairness, bias, usefulness, informativeness. This workshop builds on the success of last year's workshop at CIKM, but with a broader scope and an interactive format.
@article{arxiv.2304.07145,
title = {EvalRS 2023. Well-Rounded Recommender Systems For Real-World Deployments},
author = {Federico Bianchi and Patrick John Chia and Ciro Greco and Claudio Pomo and Gabriel Moreira and Davide Eynard and Fahd Husain and Jacopo Tagliabue},
journal= {arXiv preprint arXiv:2304.07145},
year = {2023}
}
Comments
EvalRS 2023 is a workshop at KDD23. Code and hackathon materials: https://github.com/RecList/evalRS-KDD-2023