Adaptation and Personalization for Information Visualization

* * 信息可视化（Infovis）的研究 * 传统上遵循 * 一刀切的方法，不考虑用户差异。尽管越来越多的证据表明，用户自适应交互，即适应每个用户的特定需求和能力的交互，有可能改善用户的体验，同时与可视化交互，用户差异和信息可视化中不同形式的适应的影响仍然很大程度上未被探索。为了填补这一空白，在过去的4年中，申请人从事了新的研究，为自适应可视化奠定了基础，可以帮助定制视觉显示器以满足特定的用户需求和能力。通过几项用户研究，我们发现认知能力，如感知速度，视觉和语言工作记忆，可以影响用户对不同可视化的体验，并且它们的影响是由任务复杂性介导的。这些研究还根据对通过眼动仪收集的用户注视模式的分析，确定了对这些认知能力进行不同测量的用户的表现变化的可能原因。例如，感知速度低的用户被发现在处理条形图 * 可视化中的图例时花费更多的精力，这往往会降低他们的表现。此结果 * 表明这些用户可能会受益于处理 * 图例的支持。我们还表明，可以利用凝视数据来实时预测与适应相关的用户和任务特征。这些结果 * 提供了重要的证据，表明用户自适应可视化值得 * 研究，但它们只是触及了为可视化处理提供有效个性化所需的技术诀窍的表面。因此，该提案旨在 * 对用户自适应可视化进行进一步研究，以允许 * 关闭所谓的自适应循环。特别是，我们将研究如何设计和提供个性化，是有效的和不显眼的。 我们 * 还将研究如何检测特定的用户瞬态，这些瞬态指示 * 用户在可视化处理期间最需要帮助的时间，例如 *当用户处于混乱状态时。最后，我们将研究所提出的方法的实际应用，例如 * 为阅读复杂的多模态文档提供自适应支持（例如，*经济学人》的文章），其中文字描述了 *

* * * **Research*in Information Visualization (Infovis) has traditionally followed a*one-size-fits-all approach that does not account for user differences. Despite*increasing evidence that user-adaptive*interaction, i.e. interaction adapted to suit each user's specific needs and*abilities, has the potential to improve users' experience while interacting with visualizations, the effects of both user differences and different forms of*adaptation in information visualization remain largely unexplored. In order to*fill this gap, in the last 4 years the applicant has engaged in novel research to lay the foundations for adaptive visualization that can help tailor*visual displays to specific user needs and abilities. Through several user*studies, we showed that cognitive abilities such as perceptual speed,*and visual and verbal working memory can impact user experience with*different visualizations, and that their*effect is mediated by task complexity. These studies also identified possible*reasons for the change in performance for users with different measures of*these cognitive abilities, based on the analysis of user's gaze patterns collected via an eye-trackers. For instance, users with low perceptual speed were*found to spend more effort in processing the legend in bar graph*visualizations, which tends to slow down their performance. This result*indicates that these users might benefit from support in processing *legends. We also showed that gaze data can be leveraged to predict, in real-time, user and task characteristics relevant for adaptation. These results*provided important evidence that user-adaptive visualizations are worth*investigating, but they only scratch the surface of the know-how needed to*deliver effective personalization for visualization processing. Thus, this proposal aims to*conduct further research on user-adaptive visualizations that will allow*closing the so called adaptive loop.*In particular, we will investigate how to design and deliver personalization that is effective and unobtrusive. We*will also research how to detect specific user transient states that indicate*when the user is most in need of help during visualization processing, e.g.*when the user is in a state of confusion. Finally, we will investigate practical applications of the proposed approaches, for instance*providing adaptive support for reading complex multimodal documents (e.g.,*articles from the Economist) where text describes*different aspects of th