Inferring user profile attributes from multidimensional mobile phone sensory data

User profile can be used to characterize a person and help us better understand him/her, which further can be utilized to provide enhanced personalized services. When using mobile phone, some of one's information are unavoidably and unobtrusively passed or stored, which makes it possible to draw the user profile. In this paper, we propose to infer user profile, including age, gender, and personality traits based on mobile phone sensory data. Specifically, we capture data when unlocking screen, playing games as well as some basic mobile phone information, app usage, and screen status by using common available sensors in commodity mobile phones. By analyzing the differences in users' phone usage, we extracted features for user profile inference. Random Forest regression and random forest classification models are separately used to estimate age and gender of the user while support vector regression algorithm is applied to identify personality traits. In addition, we evaluate the model through real-life experiments conducted with a total of 84 phone users. Experimental results show that our approach effective, achieving an RSME of 4.3696 in age estimation and precision of 91.70% in gender detection. As for personality traits identification, the root mean square errors of openness, conscientiousness, extraversion, agreeableness, and neuroticism are 0.29, 0.3506, 0.465, 0.3022, and 0.452, respectively.