Abstract
In the empirical sciences, the evidence is commonly manifested by experimental results. However, very often, these findings are not reproducible, hindering scientific progress. Innovations in the field of information retrieval (IR) are mainly driven by experimental results as well. While there are several attempts to assure the reproducibility of offline experiments with standardized test collections, reproducible outcomes of online experiments remain an open issue. This research project will be concerned with the reproducibility of online experiments, including real-world user feedback. In contrast to previous living lab attempts by the IR community, this project has a stronger focus on making IR systems and corresponding results reproducible. The project aims to provide insights concerning key components that affect reproducibility in online search experiments. Outcomes help to improve the design of reproducible IR online experiments in the future.
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Breuer, T. (2020). Reproducible online search experiments. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12036 LNCS, pp. 597–601). Springer. https://doi.org/10.1007/978-3-030-45442-5_77
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