Inferring User Traits from Applications Installed on a Smart Phone

Hongdo Ki, Jaehong Lee, Heewoong Park, Moon-jung Chae, Sangwoo Choi, Jonghun Park

http://doi.org/10.5626/JOK.2018.45.12.1240

Needs for customized services are increasing as a smart phone personalized device, has been used generally. Demographic information is beneficial for customized services, so inferring user traits based various data using statistical learning has been actively studied. This study conducted experiments of inferring user traits with a list of installed applications differed by users’ interest and lifestyle, and may can be accessed easily as a snapshot without explicit permission. Four feature vectors are used for inferring user traits, including vectors using application category or description that can be collected from the application market. Especially, one of the feature vectors is generated by applying Doc2Vec, a text embedding method based on a neural network, to application description. The application selection method we proposed is also used to achieve better performances than could be achieved by using all applications on the list. Last, we collected 100 lists of installed applications for experiments of inferring gender, age, relationship status, residential type, living together or not, income, outcome, height, weight, religion, semester and college, and confirmed effectiveness of proposed feature vectors and the application selection method.