Find it if you can: a game for modeling different types of web search success using interaction data
Find it if you can: a game for modeling different types of web search success using interaction data
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DOI：
10.1145/2009916.2009965
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发表时间：
2011-07
期刊：
影响因子：
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通讯作者：
Mikhail S. Ageev;Qi Guo;Dmitry Lagun;Eugene Agichtein
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文献类型：
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作者：
Mikhail S. Ageev;Qi Guo;Dmitry Lagun;Eugene Agichtein
更好地了解成功搜索者的策略和行为对于改善所有搜索者的体验至关重要。然而，搜索行为的研究一直在努力应对相对较小但可控的实验室研究和大规模的基于日志的研究之间的紧张关系，在这些研究中，搜索者的意图和许多其他重要因素必须得到推断。我们提出了我们的解决方案，用于对搜索者行为进行受控的、但现实的、可扩展的和可重复的研究。我们专注于困难的信息任务，这往往会让许多使用当前网络搜索技术的用户感到沮丧。首先，我们提出了不同类型的信息搜索“成功”的原则性形式化，它封装和锐化了以前提出的模型。其次，我们提出了一个可扩展的类似游戏的基础设施，用于众包搜索行为研究，特别是针对已知意图的信息任务捕获和评估成功的搜索策略。第三，我们使用这些数据报告了我们对搜索成功的分析，这证实并扩展了先前的发现。最后，我们证明了我们的模型可以比现有的最先进的方法更有效地预测搜索成功，无论是在我们的数据上还是在从常规搜索引擎会话中收集的不同的日志数据集上。总而言之，我们的搜索成功模型、数据收集基础设施和相关的行为分析技术极大地推动了对网络搜索成功的研究。
A better understanding of strategies and behavior of successful searchers is crucial for improving the experience of all searchers. However, research of search behavior has been struggling with the tension between the relatively small-scale, but controlled lab studies, and the large-scale log-based studies where the searcher intent and many other important factors have to be inferred. We present our solution for performing controlled, yet realistic, scalable, and reproducible studies of searcher behavior. We focus on difficult informational tasks, which tend to frustrate many users of the current web search technology. First, we propose a principled formalization of different types of "success" for informational search, which encapsulate and sharpen previously proposed models. Second, we present a scalable game-like infrastructure for crowdsourcing search behavior studies, specifically targeted towards capturing and evaluating successful search strategies on informational tasks with known intent. Third, we report our analysis of search success using these data, which confirm and extends previous findings. Finally, we demonstrate that our model can predict search success more effectively than the existing state-of-the-art methods, on both our data and on a different set of log data collected from regular search engine sessions. Together, our search success models, the data collection infrastructure, and the associated behavior analysis techniques, significantly advance the study of success in web search.