Eye-2-I: Eye-tracking for just-in-time implicit user profiling

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...

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Bibliographic Details
Published in2017 IEEE 2nd International Conference on Signal and Image Processing (ICSIP) pp. 311 - 315
Main Authors Keng-Teck Ma, Qianli Xu, Lim, Rosary, Liyuan Li, Sim, Terence, Kankanhalli, Mohan
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2017
Subjects
Advertising
classification
Electronic mail
eye-gaze
Face
Feature extraction
machine learning
Monitoring
Social network services
user profiling
Videos
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Summary: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.
DOI:10.1109/SIPROCESS.2017.8124555