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.
EvalRS 2023: Well-Rounded Recommender Systems for Real-World Deployments / Bianchi, F.; Chia, P. J.; Tagliabue, J.; Greco, C.; Moreira, G. S. P.; Eynard, D.; Husain, F.; Pomo, C.. - (2023), pp. 5851-5852. ( 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2023 usa 2023) [10.1145/3580305.3599222].
EvalRS 2023: Well-Rounded Recommender Systems for Real-World Deployments
Pomo C.
2023
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
