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.]
@article{arxiv.1507.04441,
title = {Eye-2-I: Eye-tracking for just-in-time implicit user profiling},
author = {Keng-Teck Ma and Qianli Xu and Liyuan Li and Terence Sim and Mohan Kankanhalli and Rosary Lim},
journal= {arXiv preprint arXiv:1507.04441},
year = {2016}
}
Comments
A bug was found in the codes which resulted in information leak. New experimental results will be updated at a later date. I assume all responsibility for this mistake. KT Ma