  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. We propose a novel just-in-time implicit
profiling method, Eye-2-I, which learns the user's interests, demographic and
personality traits from the eye-tracking data while the user is watching
videos. Although seemingly conspicuous by closely monitoring the user's eye
behaviors, our method is unobtrusive and privacy-preserving owing to its unique
characteristics, including (1) fast speed - the profile is available by the
first video shot, typically few seconds, and (2) self-contained - not relying
on historical data or functional modules. [Bug found. As a proof-of-concept,
our method is evaluated in a user study with 51 subjects. It achieved a mean
accuracy of 0.89 on 37 attributes of user profile with 9 minutes of
eye-tracking data.]
