[2304.07145] EvalRS 2023. Well-Rounded Recommender Systems For Real-World Deployments
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Computer Science > Information Retrieval

arXiv:2304.07145 (cs)
[Submitted on 14 Apr 2023 (v1), last revised 22 Jul 2023 (this version, v4)]

Title:EvalRS 2023. Well-Rounded Recommender Systems For Real-World Deployments

Authors:Federico Bianchi, Patrick John Chia, Ciro Greco, Claudio Pomo, Gabriel Moreira, Davide Eynard, Fahd Husain, Jacopo Tagliabue
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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.
Comments: EvalRS 2023 is a workshop at KDD23. Code and hackathon materials: this https URL
Subjects: Information Retrieval (cs.IR); Computers and Society (cs.CY)
Cite as: arXiv:2304.07145 [cs.IR]
  (or arXiv:2304.07145v4 [cs.IR] for this version)
  https://doi.org/10.48550/arXiv.2304.07145

Submission history

From: Jacopo Tagliabue [view email]
[v1] Fri, 14 Apr 2023 14:13:37 UTC (67 KB)
[v2] Wed, 19 Apr 2023 13:08:16 UTC (67 KB)
[v3] Tue, 30 May 2023 21:23:23 UTC (66 KB)
[v4] Sat, 22 Jul 2023 07:54:02 UTC (68 KB)
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