Adaptation and Personalization for Information Visualization

信息可视化的适应和个性化

• 批准号: RGPIN-2016-04611
• 负责人: Conati, Cristina
• 金额: $ 3.35万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=615179
• 关键词: Adaptation; Personalization; Information; Visualization

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 the embedded graphs.

研究 在信息可视化(InfoVis)中，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的交互以及 能力，有可能在与可视化交互时改善用户体验，用户差异和不同形式的 信息可视化中的适应在很大程度上仍未得到探索。为了 填补这一空白，在过去的4年里，申请人从事了新颖的研究，为自适应可视化奠定了基础，这有助于定制 直观地显示特定用户的需求和能力。通过多个用户 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，并且他们的 效应受任务复杂性的影响。这些研究还确定了可能的 不同衡量标准的用户性能发生变化的原因 这些认知能力是基于对用户通过眼球跟踪器收集的凝视模式的分析而得出的。例如，感知速度低的用户是 发现在处理条形图中的图例时花费了更多的精力 视觉化，这往往会降低他们的表现。这个结果 指示这些用户可能受益于处理过程中的支持 传奇人物。我们还表明，凝视数据可以被用来实时预测与适应相关的用户和任务特征。这些结果 提供了重要证据，表明用户自适应可视化值得 进行调查，但他们只触及了所需技术的皮毛 为可视化处理提供有效的个性化。因此，这项提案旨在 对用户自适应可视化进行进一步研究，以实现 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有效和不引人注目的个性化。我们 我还将研究如何检测特定的用户瞬时状态，这些状态指示 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 为阅读复杂的多模式文档提供自适应支持(例如， 《经济学人》的文章)，其中文本描述 嵌入图形的不同方面。