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Understanding and Leveraging the Impact of Response Latency on User Behaviour in Web Search

机译:了解和利用响应延迟对Web搜索中用户行为的影响

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The interplay between the response latency of web search systems and users" search experience has only recently started to attract research attention, despite the important implications of response latency on mon-etisation of such systems. In this work, we carry out two complementary studies to investigate the impact of response latency on users' searching behaviour in web search engines. We first conduct a controlled user study to investigate the sensitivity of users to increasing delays in response latency. This study shows that the users of a fast search system are more sensitive to delays than the users of a slow search system. Moreover, the study finds that users are more likely to notice the response latency delays beyond a certain latency threshold, their search experience potentially being affected. We then analyse a large number of search queries obtained from Yahoo Web Search to investigate the impact of response latency on users' click behaviour. This analysis demonstrates the significant change in click behaviour as the response latency increases. We also find that certain user, context, and query attributes play a role in the way increasing response latency affects the click behaviour. To demonstrate a possible use case for our findings, we devise a machine-learning framework that leverages the latency impact, together with other features, to predict whether a user will issue any clicks on web search results. As a further extension of this use case, we investigate whether this machine-learning framework can be exploited to help search engines reduce their energy consumption during query processing.
机译:尽管响应等待时间对此类系统的市场化具有重要意义,但Web搜索系统的响应等待时间与用户搜索体验之间的相互作用直到最近才开始引起研究关注。在这项工作中,我们进行了两项补充研究为了调查响应潜伏期对网络搜索引擎中用户搜索行为的影响,我们首先进行了一项受控用户研究,以调查用户对响应潜伏期增加的延迟的敏感性,该研究表明快速搜索系统的用户更多与慢搜索系统的用户相比,它对延迟敏感;此外,研究发现,用户更有可能注意到超过一定延迟阈值的响应延迟,这可能会影响他们的搜索体验,然后我们分析了大量搜索查询是从Yahoo Web Search获得的,用于调查响应延迟对用户点击行为的影响。点击行为的显着变化是响应延迟的增加。我们还发现某些用户,上下文和查询属性在增加响应延迟影响点击行为的方式中发挥了作用。为了演示我们的发现的可能用例,我们设计了一种机器学习框架,该框架利用延迟影响以及其他功能来预测用户是否会在Web搜索结果上发出任何点击。作为此用例的进一步扩展,我们研究了是否可以利用此机器学习框架来帮助搜索引擎减少查询处理期间的能耗。

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