Adaptation and Personalization for Information Visualization

研究 信息可视化（Infovis）传统上遵循 一刀切的方法，没有考虑到用户的差异。尽管 越来越多的证据表明， 互动，即适合每个用户具体需求的互动， 能力，有可能改善用户的体验，同时与可视化交互，用户差异和不同形式的 适应信息可视化仍然在很大程度上未被探索。为了 为了填补这一空白，在过去的4年里，申请人从事了新的研究，为自适应可视化奠定了基础， 视觉显示器，以满足特定的用户需求和能力。通过几个用户 研究表明，认知能力，如感知速度， 视觉和语言工作记忆可以影响用户体验， 不同的可视化，他们的 任务复杂度是影响任务复杂度的中介变量。这些研究还发现， 使用不同计量标准的用户的业绩变化原因 这些认知能力，基于对通过眼动仪收集的用户注视模式的分析。例如，感知速度低的用户 发现在处理条形图中的图例时花费更多的精力 可视化，这往往会降低他们的表现。这一结果 表示这些用户可能受益于处理过程中的支持 传说我们还表明，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 调查，但他们只触及了所需的技术诀窍的表面， 为可视化处理提供有效的个性化。因此，本建议旨在 对用户自适应可视化进行进一步研究， 关闭所谓的自适应环路。 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 我还将研究如何检测特定的用户瞬态， 当用户在可视化处理期间最需要帮助时，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）， 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