A First Step toward Recommendations Based on the Memory of Users
Information visualization systems have traditionally followed a one-size-fits-all paradigm with respect to their users, i.e., their design is seldom personalized to the specific characteristics of users (e.g. perceptual abilities) or their tasks (e.g. task difficulty). In view of creating information visualization systems that can adapt to each individual user and task, this paper provides an analysis of user eye gaze data aimed at identifying behavioral patterns that are specific to certain user and task groups. In particular, the paper leverages the sequential nature of user eye gaze patterns through differential sequence mining, and successfully identifies a number of pattern differences that could be leveraged by adaptive information visualization systems in order to automatically identify (and consequently adapt to) different user and task characteristics.