The State of the Art in User-Adaptive Visualizations

Abstract

Research shows that user traits can modulate the use of visualization systems and have a measurable influence on users' accuracy, speed, and attention when performing visual analysis. This highlights the importance of user-adaptive visualization that can modify themselves to the characteristics and preferences of the user. However, there are very few such visualization systems, as creating them requires broad knowledge from various sub-domains of the visualization community. A user-adaptive system must consider which user traits they adapt to, their adaptation logic and the types of interventions they support. In this STAR, we survey a broad space of existing literature and consolidate them to structure the process of creating user-adaptive visualizations into five components: Capture Ⓐ Input from the user and any relevant peripheral information. Perform computational Ⓑ User Modelling with this input to construct a Ⓒ User Representation. Employ Ⓓ Adaptation Assignment logic to identify when and how to introduce Ⓔ Interventions. Our novel taxonomy provides a road map for work in this area, describing the rich space of current approaches and highlighting open areas for future work.