Validating Synthetic Usage Data in Living Lab Environments

IF 1.5 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS ACM Journal of Data and Information Quality Pub Date : 2023-09-24 DOI:10.1145/3623640
Timo Breuer, Norbert Fuhr, Philipp Schaer
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Abstract

Evaluating retrieval performance without editorial relevance judgments is challenging, but instead, user interactions can be used as relevance signals. Living labs offer a way for small-scale platforms to validate information retrieval systems with real users. If enough user interaction data are available, click models can be parameterized from historical sessions to evaluate systems before exposing users to experimental rankings. However, interaction data are sparse in living labs, and little is studied about how click models can be validated for reliable user simulations when click data are available in moderate amounts. This work introduces an evaluation approach for validating synthetic usage data generated by click models in data-sparse human-in-the-loop environments like living labs. We ground our methodology on the click model's estimates about a system ranking compared to a reference ranking for which the relative performance is known. Our experiments compare different click models and their reliability and robustness as more session log data becomes available. In our setup, simple click models can reliably determine the relative system performance with already 20 logged sessions for 50 queries. In contrast, more complex click models require more session data for reliable estimates, but they are a better choice in simulated interleaving experiments when enough session data are available. While it is easier for click models to distinguish between more diverse systems, it is harder to reproduce the system ranking based on the same retrieval algorithm with different interpolation weights. Our setup is entirely open, and we share the code to reproduce the experiments.
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在生活实验室环境中验证合成使用数据
在没有编辑相关性判断的情况下评估检索性能是具有挑战性的,但相反,用户交互可以用作相关性信号。生活实验室为小规模平台提供了一种与真实用户验证信息检索系统的方法。如果有足够的用户交互数据可用,点击模型可以从历史会话参数化,以便在向用户展示实验排名之前评估系统。然而,在真实的实验室中,交互数据是稀疏的,并且很少有人研究当点击数据数量适中时,如何验证点击模型以进行可靠的用户模拟。这项工作介绍了一种评估方法,用于验证在数据稀疏的人在环环境(如生活实验室)中由点击模型生成的综合使用数据。我们的方法基于点击模型对系统排名的估计,并与相对性能已知的参考排名进行比较。随着会话日志数据越来越多,我们的实验比较了不同的点击模型及其可靠性和鲁棒性。在我们的设置中,简单的单击模型可以可靠地确定50个查询的20个记录会话的相对系统性能。相比之下,更复杂的点击模型需要更多的会话数据来进行可靠的估计,但在模拟交错实验中,当有足够的会话数据可用时,它们是更好的选择。虽然点击模型更容易区分更多不同的系统,但基于相同的检索算法,使用不同的插值权重来再现系统排名是比较困难的。我们的设置是完全开放的,我们共享代码来重现实验。
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来源期刊
ACM Journal of Data and Information Quality
ACM Journal of Data and Information Quality COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
4.10
自引率
4.80%
发文量
0
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