How to Measure the Reproducibility of System-oriented IR Experiments
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Abstract (arXiv)
Replicability and reproducibility of experimental results are primary concerns in all the areas of science and IR is not an exception. Besides the problem of moving the field towards more reproducible experimental practices and protocols, we also face a severe methodological issue: we do not have any means to assess when reproduced is reproduced. Moreover, we lack any reproducibility-oriented dataset, which would allow us to develop such methods. To address these issues, we compare several measures to objectively quantify to what extent we have replicated or reproduced a system-oriented IR experiment. These measures operate at different levels of granularity, from the fine-grained comparison of ranked lists, to the more general comparison of the obtained effects and significant differences. Moreover, we also develop a reproducibility-oriented dataset, which allows us to validate our measures and which can also be used to develop future measures.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Oct 2020.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 0; 2024: 1; 2025: 2; 2026: 1.
6 papers.
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- 2026When Do Anchor-Based Pointwise LLM Rerankers Help? Retriever Quality, Statistical Scope, and Anchor Designcited by 0
- 2026When Do Anchor-Based Pointwise LLM Rerankers Help? Retriever Quality, Statistical Scope, and Anchor Design11 Aug 2026
- 2025REANIMATOR: Reanimate Retrieval Test Collections with Extracted and Synthetic Resources10 Apr 2025
- 2025Reproducibility and Artifact Consistency of the SIGIR 2022 Recommender Systems Papers Based on Message Passing10 Mar 2025
- 2024A Reproducibility Study of PLAID23 Apr 2024
- 2021Trustworthy AI: From Principles to Practices4 Oct 2021
- 2021A Next Basket Recommendation Reality Check29 Sep 2021