Eye-2-I: Eye-tracking for just-in-time implicit user profiling
Keng-Teck Ma, Qianli Xu, Rosary Yuting Lim, Liyuan Li, Terence Sim, Mohan Kankanhalli · 2017
For many applications, such as targeted advertising and content recommendation, knowing users' traits and interests is a prerequisite. User profiling is a helpful approach for this purpose. However, current methods, i.e. self-reporting, web-activity monitoring and social media mining are either intrusive or require data over long periods of time. Recently, there is growing evidence in cognitive science that a variety of users' profile is significantly correlated with eye-tracking data. A novel just-in-time implicit profiling method, Eye-2-I, which learns the user's demographic and personality traits from the eye-tracking data while the user is watching videos is proposed. Although seemingly conspicuous by closely monitoring the user's eye behaviors, the proposed method is unobtrusive and privacy-preserving owing to its unique combination of speed and implicitness. As a proof-of-concept, the proposed method is evaluated in a user study with 51 subjects.