User behavior models for information access evaluation

为了改进搜索引擎和其他信息访问系统，必须以人为本来衡量它们的性能。一个关键的衡量标准是找到所需信息和完成手头任务所需的时间。无论用户是在搜索单个事实，还是在研究复杂的问题，用户都希望尽可能快地满足他们的信息。研究人员和工程师都需要一种方法来快速和负担得起的估计率，用户可以满足他们的信息需求与给定的搜索引擎。为了准确估计用户性能，必须设计有效性度量，以适应不同的用户任务和计算设备。例如，搜索简单事实的用户与研究复杂问题的用户的行为预期不同。同样，用于访问信息的设备可能会对用户的行为产生重大影响。对于移动的搜索，需要考虑诸如小屏幕和虚拟键盘之类的设备约束。传统的有效性措施是不适合估计用户的有效性在不同的用户任务和访问设备，因为他们缺乏用户行为模型，这阻止了他们适应搜索上下文。这项工作提出了进一步发展的时间偏置增益有效性的措施，它可以适应搜索上下文。

To improve search engines, and other information access systems, their performance must be measured in human terms. A key measure is the time taken to find required information and to complete the task at hand. Whether a user is searching for a single fact, or is researching a complex problem, the user wants to satisfy their information as fast as possible. Both researchers and engineers need a means to quickly and affordably estimate the rate at which users can satisfy their information needs with a given search engine. To produce accurate estimates of user performance, effectiveness measures must be designed that can adjust to different user tasks and computing devices. For example, users searching for a simple fact are expected to behave differently than users researching a complex problem. Likewise, the device used to access information can have a significant impact on how a user behaves. For mobile search, device constraints such as a small screen and a virtual keyboard need to be taken into consideration. Traditional effectiveness measures are ill-suited to estimate user effectiveness across different user tasks and access devices given their lack of user behavior models, which prevents adapting them to the search context. This work proposes to further develop the time-biased gain effectiveness measure, which can be adapted to the search context.