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Continuous Automated Reproducibility: Practical Tools and Workflows for Repeatable and Reproducible Research
Conference proceeding   Open access

Continuous Automated Reproducibility: Practical Tools and Workflows for Repeatable and Reproducible Research

Michael D. Ekstrand
Proceedings of the 20th ACM Conference on Recommender Systems, pp 1900-1901
27 Sep 2026
DOI: https://doi.org/10.1145/3773078.3831854
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https://doi.org/10.1145/3773078.3831854View
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Abstract

General and reference General and reference -- Cross-computing tools and techniques General and reference -- Cross-computing tools and techniques -- Experimentation Human-centered computing Human-centered computing -- Collaborative and social computing Human-centered computing -- Collaborative and social computing -- Collaborative and social computing theory, concepts and paradigms Human-centered computing -- Collaborative and social computing -- Collaborative and social computing theory, concepts and paradigms -- Collaborative filtering Human-centered computing -- Collaborative and social computing -- Collaborative and social computing theory, concepts and paradigms -- Social recommendation Information systems Information systems -- Information retrieval Information systems -- Information retrieval -- Retrieval tasks and goals Information systems -- Information retrieval -- Retrieval tasks and goals -- Recommender systems Information systems -- Information systems applications Information systems -- World Wide Web Information systems -- World Wide Web -- Web mining Information systems -- World Wide Web -- Web searching and information discovery Mathematics of computing Mathematics of computing -- Probability and statistics Mathematics of computing -- Probability and statistics -- Statistical paradigms Mathematics of computing -- Probability and statistics -- Statistical paradigms -- Exploratory data analysis
While the RecSys community has been promoting and discussing the importance of reproducible research for many years, there remains a significant lack of practical resources to help RecSys researchers and practitioners, particularly those just getting started in the field, to actually build reproducible experiments and research workflows. This hands-on tutorial fills that gap. Based on the idea of “continuous reproducibility”, I present open-source tools and practical experimental design and implementation techniques to build recommender systems experiments that are reproducible from Day 1. This tutorial will be useful for researchers at various stages who want to make their work more reproducible, as well as to industry practitioners and researchers who want to build reliable, adaptable, and reproducible pipelines top train and evaluate recommender systems for production applications.

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