| Vol-3450 urn:nbn:de:0074-3450-6 Copyright © 2023 for the individual papers by the papers' authors. Copyright © 2023 for the volume as a collection by its editors. This volume and its papers are published under the Creative Commons License Attribution 4.0 International (CC BY 4.0).

# EvalRS 2023
EvalRS: A Rounded Evaluation Of Recommender Systems 2023

### Proceedings of EvalRS: A Rounded Evaluation Of Recommender Systems 2023
co-located with 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (SIGKDD 2023)

### Long Beach,CA, USA, August 7, 2023.

Edited by

### Federico Bianchi *
Patrick John Chia **
Ciro Greco ***
Claudio Pomo ****
Gabriel Moreira *****
Davide Eynard ******
Fahd Husain ******
Jacopo Tagliabue *******

* Stanford University
** Coveo
*** Bauplan
**** Politecnico di Bari
***** NVIDIA
****** mozilla.ai
****** New York University

## Table of Contents

- EvalRS 2023. Well-Rounded Recommender Systems For Real-World Deployments (preface)
  Federico Bianchi, Patrick John Chia, Ciro Greco, Claudio Pomo, Gabriel Moreira, Davide Eynard, Fahd Husain, Jacopo Tagliabue
  Summary: There were 7 papers submitted for peer-review to this workshop. Out of these, 5 papers were accepted for this volume, 3 as regular papers and 2 as short papers.

### Keynotes

- Practical Considerations for Responsible RecSys
  Luca Belli
- Ask and answer: A case study in ML evaluation at Snap
  Joey Robinson

### Paper Session

- Realistic but Non-Identifiable Synthetic User Data Generation
  Isaac Noble, Ivan Vendrov, Xin Xu, Deepak Ramachandran
- Disentangling the Performance Puzzle of Multimodal-aware Recommender Systems
  Daniele Malitesta, Giandomenico Cornacchia, Claudio Pomo, Tommaso Di Noia
- Bridging Offline-Online Evaluation with a Time-dependent and Popularity Bias-free Offline Metric for Recommenders
  Petr Kasalický, Rodrigo Alves, Pavel Kordík
- Evaluating Recommendation Systems Using the Power of Embeddings (short paper)
  Jonathan Gomes Selman, Robert Friel, Bogdan Gheorghe, Derek Austin, Atindriyo Sanyal
- Metric@CustomerN: Evaluating Metrics at a Customer Level in E-Commerce (short paper)
  Mayank Singh, Emily Ray, Marc Ferradou, Andrea Barraza-Urbina

2023-08-06: submitted by Claudio Pomo, metadata incl. bibliographic data published under Creative Commons CC0
2023-08-07: published on CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073) |valid HTML5|