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 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.

[1]  Ana-Maria Popescu,et al.  A Machine Learning Approach to Twitter User Classification , 2011, ICWSM.

[2]  Julie E. Boland,et al.  Cultural variation in eye movements during scene perception. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[3]  Alessandro Perina,et al.  Unveiling the multimedia unconscious: implicit cognitive processes and multimedia content analysis , 2013, ACM Multimedia.

[4]  Hideki Asoh,et al.  A Context-Aware Movie Preference Model Using a Bayesian Network for Recommendation and Promotion , 2007, User Modeling.

[5]  Mohammad Soleymani,et al.  A Multimodal Database for Affect Recognition and Implicit Tagging , 2012, IEEE Transactions on Affective Computing.

[6]  Behavioral inhibition and electrodermal activity during deception. , 1985, Journal of personality and social psychology.

[7]  Alireza Sahami Shirazi,et al.  Increasing the user's attention on the web: using implicit interaction based on gaze behavior to tailor content , 2012, NordiCHI.

[8]  Nicu Sebe,et al.  Personalized multimedia retrieval: the new trend? , 2007, MIR '07.

[9]  Cristina Conati,et al.  Eye-tracking for user modeling in exploratory learning environments: An empirical evaluation , 2007, Knowl. Based Syst..

[10]  M. Bradley,et al.  The pupil as a measure of emotional arousal and autonomic activation. , 2008, Psychophysiology.

[11]  N. Davey,et al.  Principal component analysis of gender, ethnicity, age and identity of face images , 2005 .

[12]  Qiang Ji,et al.  In the Eye of the Beholder: A Survey of Models for Eyes and Gaze , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  L. Itti,et al.  Top-down influences on visual attention during listening are modulated by observer sex , 2012, Vision Research.

[14]  T. Graepel,et al.  Private traits and attributes are predictable from digital records of human behavior , 2013, Proceedings of the National Academy of Sciences.

[15]  Andrew J. Stewart,et al.  Integrating text and pictorial information: eye movements when looking at print advertisements. , 2001, Journal of experimental psychology. Applied.

[16]  Eli Peli,et al.  Where people look when watching movies: Do all viewers look at the same place? , 2007, Comput. Biol. Medicine.

[17]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[18]  G. Bodenhausen,et al.  Personalized Persuasion , 2012, Psychological science.

[19]  Harish Katti,et al.  CAVVA: Computational Affective Video-in-Video Advertising , 2014, IEEE Transactions on Multimedia.

[20]  Boris E. R. de Ruyter,et al.  Personalizing persuasive technologies: Explicit and implicit personalization using persuasion profiles , 2015, Int. J. Hum. Comput. Stud..

[21]  Nicola C. Anderson,et al.  The influence of personality on social attention , 2014 .

[22]  D. Bors,et al.  Eye-movement analysis demonstrates strategic influences on intelligence , 2006 .

[23]  L. Martens.  Automatic person and personality recognition from facial expressions , 2012 .

[24]  Anthony F. Norcio,et al.  Cultural differences on attention and perceived usability: Investigating color combinations of animated graphics , 2006, Int. J. Hum. Comput. Stud..

[25]  C. G. Jung.  Psychological Types , 2000 .

[26]  Yi Yang,et al.  Ranking with local regression and global alignment for cross media retrieval , 2009, ACM Multimedia.

[27]  Lin Qiu,et al.  You are what you tweet: Personality expression and perception on Twitter , 2012 .

[28]  Mohan S. Kankanhalli,et al.  VIP: A Unifying Framework for Computational Eye-Gaze Research , 2013, HBU.

[29]  Chi-Chun Lo,et al.  Personalized blog content recommender system for mobile phone users , 2010, Int. J. Hum. Comput. Stud..

[30]  Stefan Winkler,et al.  Overview of Eye tracking Datasets , 2013, 2013 Fifth International Workshop on Quality of Multimedia Experience (QoMEX).