III: Small: Search Assistance Using Search Trails

当前的搜索系统在帮助用户完成简单的搜索任务方面是有效的，但对复杂任务提供的支持较少。当用户搜索信息时，他们会根据与搜索系统的交互创建“搜索路径”。这些踪迹具有有价值的信息，可以使未来的搜索者在类似的任务上受益，并且可以包括发出的查询、点击的结果、查看的页面、书签标记的页面以及由先前的搜索者所做的注释。目前，许多搜索引擎使用这些活动跟踪来改进其搜索算法和结果。然而，目前的方法是有限的，他们可能会导致间接的好处，但不考虑搜索线索的全部潜力作为一种直接形式的搜索援助。这项研究将开发和评估系统，自动显示相关的搜索线索，作为一种形式的搜索帮助用户。使用搜索路径进行搜索辅助有可能改进广泛的系统，包括数百万人使用的网络搜索引擎、数字图书馆以及为具有相似目标和需求的用户提供服务的企业和网站特定搜索引擎。这一项目的成果将扩大搜索在广泛领域的可及性。该研究项目将产生数据，使其他研究人员能够开发和评估自己的解决方案和工具，以促进其他人对搜索行为进行大规模研究。从这些研究中获得的见解也将引起心理学和教育等其他领域研究人员的兴趣。以前的研究表明搜索线索的有用性，但没有回答设计和实施它们所需的关键研究挑战。系统需要预测何时向用户显示搜索踪迹，显示哪些踪迹，以及如何以支持用户目标的方式显示它们。将分三个阶段应对这些挑战。第一阶段将确定用户、任务和系统的哪些因素会影响用户是否需要帮助，出于什么目的，以及他们是否能够获得有用的信息。这些结果将对跨各种领域的援助系统的设计产生影响。第二阶段将开发模型，用于预测何时根据用户和任务特征向用户显示踪迹，以及指示跟踪器是否有困难的行为测量。所获得的见解将有助于开发更具用户和任务意识的搜索引擎。最后，第三阶段将开发模型，用于预测当前搜索会话显示哪些踪迹。能够匹配搜索会话的基础上，用户的更高层次的目标有直接的影响，其他信息检索任务，如文档排名，查询建议，和聚合搜索。我们将使用学习排名算法来结合联合收割机特征，这些特征测量当前搜索会话与候选线索之间的相似性，以及线索中的信息内容。

Current search systems are effective in helping users complete simple search tasks, but provide less support for complex tasks. When users search for information, they create "search trails" based on their interactions with the search system. These trails have valuable information that could benefit a future searcher working on a similar task and may include the queries issued, results clicked, pages viewed, pages bookmarked, and annotations made by a previous searcher. Currently, many search engines use these activity traces to improve their search algorithms and results. However, current approaches are limited; they may lead to indirect benefits, but do not consider the full potential of search trails as a direct form of search assistance. This research will develop and evaluate systems that automatically display relevant search trails as a form of search assistance to users. Using search trails for search assistance has the potential to improve a broad range of systems, including web search engines used by millions, digital libraries, and enterprise and website-specific search engines that serve users with similar goals and needs. The outcomes of this project will expand the accessibility of search across a wide range of domains. The research project will produce data that will allow other researchers to develop and evaluate their own solutions and tools that will facilitate others to perform large-scale studies of search behavior. Insights gained from the studies will also be of interest to researchers in other fields such as psychology and education. Prior research on search trails has suggested the usefulness of search trails, but has not answered key research challenges required to design and implement them. The system needs to predict when to display search trails to a user, which trails to display, and how to display them in a way that supports the user's goal. These challenges will be addressed in three phases. Phase 1 will determine which factors of the user, task, and system influence whether a searcher wants help, for what purpose, and whether they are able to gain useful information. These results will have implications for the design of assistance systems across a variety of domains. Phase 2 will develop models for predicting when to show trails to a user based on user and task features, as well as behavioral measures that indicate whether a searcher is having difficulty. The insights gained will help the development of search engines that are more user and task-aware. Finally, Phase 3 will develop models for predicting which trails to show for the current search session. Being able to match search sessions based on the user's higher-level goal has direct implications to other information retrieval tasks such as document ranking, query suggestion, and aggregated search. We will use learning-to-rank algorithms to combine features that measure the similarity between the current search session and the candidate trail, and the information content in the trail.