Lead: Cristina Conati
This project aims to apply concepts of user adaptive interaction and personalization to information visualization. We have identified a range of user traits that impact interaction with a variety of visualizations (e.g. perceptual abilities such as visual working memory and traits such as need for cognition). Based on these findings, we are investigating machine learning methods to detect these traits in real-time, as well as suitable forms of personalization driven by these detectors.