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Beyond actions: Exploring the discovery of tactics from user logs

机译：超越行动：探索用户日志中的战术发现

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摘要

Search log analysis has become a common practice to gain insights into user search behaviour: it helps gain an understanding of user needs and preferences, as well as an insight into how well a system supports such needs. Currently, log analysis is typically focused on low-level user actions, i.e. logged events such as issued queries and clicked results, and often only a selection of such events are logged and analysed. However, types of logged events may differ widely from interface to interface, making comparison between systems difficult. Further, the interpretation of the meaning of and subsequent analysis of a selection of events may lead to conclusions out of context- e.g. the statistics of observed query reformulations may be influenced by the existence of a relevance feedback component Alternatively, in lab studies user activities can be analysed at a higher level, such as search tactics and strategies, abstracted away from detailed interface implementation. Unfortunately, until now the required manual codings that map logged events to higher-level interpretations have prevented large-scale use of this type of analysis. In this paper, we propose a new method for analysing search logs by (semi-)automatically identifying user search tactics from logged events, allowing large-scale analysis that is comparable across search systems. In addition, as the resulting analysis is at a tactical level we reduce potential issues surrounding the need for interpretation of low-level user actions for log analysis. We validate the efficiency and effectiveness of the proposed tactic identification method using logs of two reference search systems of different natures: a product search system and a video search system. With the identified tactics, we perform a series of novel log analyses in terms of entropy rate of user search tactic sequences, demonstrating how this type of analysis allows comparisons of user search behaviours across systems of different nature and design. This analysis provides insights not achievable with traditional log analysis.
机译：搜索日志分析已成为获得对用户搜索行为的见解的一种常见实践：它有助于获得对用户需求和偏好的了解，以及对系统如何满足此类需求的见解。当前，日志分析通常集中于低级用户动作，即，诸如发出的查询和单击的结果之类的记录事件，并且通常仅记录和分析此类事件中的一部分。但是，不同接口之间记录的事件类型可能差别很大，这使得系统之间的比较变得困难。此外，对事件选择的含义的解释和随后的分析可能会导致脱离上下文的结论，例如：相关的反馈组件的存在可能会影响观察到的查询表述的统计数据。或者，在实验室研究中，可以从详细的界面实现中抽象出更高级别的用户活动，例如搜索策略和策略。不幸的是，到目前为止，将记录的事件映射到更高级别的解释所需的手动编码还无法大规模使用这种类型的分析。在本文中，我们提出了一种通过从记录的事件中（半）自动识别用户搜索策略来分析搜索日志的新方法，从而可以在整个搜索系统中进行大规模分析。此外，由于结果分析是在战术层面上的，因此，我们减少了对解释日志分析的低级用户操作的需求所带来的潜在问题。我们使用两个性质不同的参考搜索系统（产品搜索系统和视频搜索系统）的日志来验证所提出的战术识别方法的效率和有效性。借助确定的策略，我们根据用户搜索策略序列的熵率执行了一系列新颖的日志分析，展示了这种类型的分析如何使不同性质和设计的系统之间的用户搜索行为进行比较。该分析提供了传统日志分析无法实现的见解。

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