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 reproducibilityoriented dataset, which allows us to validate our measures and which can also be used to develop future measures. CCS CONCEPTS • Information systems → Evaluation of retrieval results; Retrieval effectiveness;
We propose a family of new evaluation measures, called Markov Precision (MP), which exploits continuous-time and discrete-time Markov chains in order to inject user models into precision. Continuous-time MP behaves like timecalibrated measures, bringing the time spent by the user into the evaluation of a system; discrete-time MP behaves like traditional evaluation measures. Being part of the same Markovian framework, the time-based and rank-based versions of MP produce values that are directly comparable. We show that it is possible to recreate average precision using specific user models and this helps in providing an explanation of Average Precision (AP) in terms of user models more realistic than the ones currently used to justify it. We also propose several alternative models that take into account different possible behaviors in scanning a ranked result list. Finally, we conduct a thorough experimental evaluation of MP on standard TREC collections in order to show that MP is as reliable as other measures and we provide an example of calibration of its time parameters based on click logs from Yandex.
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