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QueryCrumbs search query history visualization - Usability, transparency and long-term usage

机译:QueryCrumbs搜索查询历史记录可视化 - 可用性,透明度和长期使用

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摘要

Models of human information seeking reveal that search, in particular ad-hoc retrieval, is non-linear and iterative. Despite these findings, today's search user interfaces do not support non-linear navigation, like for example backtracking in time. We propose QueryCrumbs, a compact and easy-to-understand visualization for navigating the search query history supporting iterative query refinement. We apply a multi-layered interface design to support novices and first-time users as well as intermediate and expert users. The visualization is evaluated with novice users in a formative user study, with experts in a think aloud test and its usage in a long-term study with software logging. The formative evaluation showed that the interactions can be easily performed, and the visual encodings were well understood without instructions. Results indicate that QueryCrumbs can support users when searching for information in an iterative manner. The evaluation with experts showed that expert users can gain valuable insights into the back-end search engine by identifying specific patterns in the visualization. In a long-term usage study, we observed an uptake of the visualization, indicating that users deem QueryCrumbs beneficial for their search interactions.
机译:寻求人类信息的模型揭示搜索,特别是临时检索,是非线性和迭代的。尽管有这些发现,今天的搜索用户界面不支持非线性导航,例如及时回溯。我们提出QueryCrumbs,一个紧凑且易于理解的可视化,用于导航支持迭代查询细化的搜索查询历史记录。我们应用多层界面设计,以支持新手和首次用户以及中级和专家用户。在形成的用户学习中,使用新手用户评估了可视化,专家们在大声测试中,在一个长期的研究中思考和使用软件记录。形成性评价表明,可以容易地进行相互作用,并且在没有指令的情况下很好地理解视觉编码。结果表明,在以迭代方式搜索信息时,QueryCrumbs可以支持用户。与专家的评估表明,专家用户可以通过识别可视化中的特定模式来获得对后端搜索引擎的有价值的见解。在一个长期使用的研究中,我们观察了可视化的摄取,表明用户认为QueryCrumbs有利于他们的搜索交互。

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