Information visualization is an important mode of human-computer interaction to acquire insights of complex datasets. However, a uniform visualization format may be inefficient to bring the insights to different users at different contexts due to the users' differences in knowledge, perception, and cognition capabilities. On the other hand, typical visualization evaluation methods were conducted in an offline manner hence reducing the efficiency. Thus, a real-time and personalized visualization recommendation approach is needed. This intellectual property provides a real-time visualization recommendation methodology to select the best designs for different users at different contexts (i.e., tasks) by extracting user mental and physical profiles from wearable sensing devices and user input behaviors.
