Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Challenge Accepted
  • Published:

A challenge for rounded evaluation of recommender systems

An Author Correction to this article was published on 11 March 2024

This article has been updated

The organizers of the EvalRS recommender systems competition argue that accuracy should not be the only goal and explain how they took robustness and fairness into account.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: A radar chart showcasing performance across the main metric types.

Change history


  1. Tagliabue, J. et al. Preprint at (2021).

  2. Baigorria Alonso, M. Preprint at (2021).

  3. Ribeiro, M. T., Wu, T., Guestrin, C. & Singh, S. in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics 4902–4912 (2020).

  4. Chia, P. J., Tagliabue, J., Bianchi, F., He, C. & Ko, B. In Companion Proc. Web Conference 2022 99–104 (2022).

  5. Tagliabue, J. et al. Proceedings of the CIKM 2022 Workshops co-located with 31st ACM International Conference on Information and Knowledge Management Vol. 3318 CEUR-WS (2022).

  6. Schedl, M. In Proc. 2016 ACM on International Conference on Multimedia Retrieval 103–110 (2016).

  7. Ke Yang, J. S. In Proc. 29th International Conference on Scientific and Statistical Database Management 1–6 (2017).

  8. Dixon, L., Li, J., Sorensen, J., Thain, N. & Vasserman, L. In Proc. 2018 AAAI/ACM Conference on AI, Ethics, and Society 67–73 (2018).

Download references


EvalRS is based on RecList, an open source library whose development is supported by Comet, Neptune and Gantry.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Jacopo Tagliabue.

Ethics declarations

Competing interests

The authors declare they have no conflicting interests.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tagliabue, J., Bianchi, F., Schnabel, T. et al. A challenge for rounded evaluation of recommender systems. Nat Mach Intell 5, 181–182 (2023).

Download citation

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


Quick links

Nature Briefing AI and Robotics

Sign up for the Nature Briefing: AI and Robotics newsletter — what matters in AI and robotics research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: AI and Robotics