From Variability to Stability: Advancing RecSys Benchmarking Practices-Reference-Cited by-同舟云学术

From Variability to Stability: Advancing RecSys Benchmarking Practices

Published:2024-08-24 Issue: Volume:7 Page:5701-5712
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Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Shevchenko Valeriy¹^ORCID,Belousov Nikita¹^ORCID,Vasilev Alexey²^ORCID,Zholobov Vladimir³^ORCID,Sosedka Artyom²^ORCID,Semenova Natalia⁴^ORCID,Volodkevich Anna²^ORCID,Savchenko Andrey²^ORCID,Zaytsev Alexey⁵^ORCID

Affiliation:

1. Skoltech, Moscow, Russian Federation

2. Sber AI Lab, Moscow, Russian Federation

3. Skoltech & MIPT, Moscow, Russian Federation

4. AIRI & Sber AI Lab, Moscow, Russian Federation

5. Skoltech & BIMSA, Moscow, Russian Federation

Funder

the Analytical center under the RF Government

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3671655

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3. Vito Walter Anelli, Alejandro Bellogín, Antonio Ferrara, Daniele Malitesta, Felice Antonio Merra, Claudio Pomo, Francesco Maria Donini, and Tommaso Di Noia. 2021. Elliot: A comprehensive and rigorous framework for reproducible recommender systems evaluation. In ACM SIGIR. 2405--2414.

4. Vito Walter Anelli, Tommaso Di Noia, Eugenio Di Sciascio, Claudio Pomo, and Azzurra Ragone. 2019. On the discriminative power of hyper-parameters in cross-validation and how to choose them. In ACM RecSys. 447--451.

5. Ankurnapa. 2020. RateBeer Dataset competition. https://www.kaggle.com/datasets/ankurnapa/rate-beer-data. Accessed: 2024--13--6.