Adaptation and Personalization for Information Visualization

** * **信息可视化研究（Infovis）传统上遵循一种“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，有可能在与可视化交互时改善用户的体验，但用户差异和不同形式的适应在信息可视化中的影响在很大程度上仍未被探索。为了填补这一空白，在过去的4年里，申请人一直从事新颖的研究，为自适应可视化奠定基础，帮助定制视觉显示，以满足特定的用户需求和能力。通过多项用户研究，我们发现感知速度、视觉和语言工作记忆等认知能力可以影响不同视觉化的用户体验，并且它们的影响受任务复杂性的调节。这些研究还根据对通过眼动仪收集的用户凝视模式的分析，确定了不同认知能力的用户表现变化的可能原因。例如，研究发现，感知速度较低的用户在处理条形图可视化中的图例时花费的精力更多，这往往会降低他们的表现。这个结果表明这些用户可能会从处理图例的支持中受益。我们还表明，凝视数据可以用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，证明用户自适应可视化是值得研究的，但它们只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，本提案旨在对用户自适应可视化进行进一步研究，从而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有效且不引人注目的个性化。我们还将研究如何检测特定的用户瞬态，以指示用户在可视化处理过程中何时最需要帮助，例如当用户处于混乱状态时。最后，我们将研究所提出方法的实际应用，例如为阅读复杂的多模态文档（例如，《经济学人》的文章）提供自适应支持，其中文本描述了文章的不同方面

* * * **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