Adaptation and Personalization for Information Visualization

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* * * **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