ליאור רוקח

Inferring socio-demographic attributes of users is an important and challenging task that could help with personalization, recommendation,advertising,etc.Sensor data collected from mobile devices can be utilized for inferring such attributes. Previous works have focused on combining different typesofsensors,such as applications, accelerometer, GPS, battery,and many others,to achieve this task. In this study, we were able to infer attributes,such as gender, age, marital status, and whether the user has children, using solely the GPS sensor. We suggest a novel inference technique, which learns an embeddingrepresentation of preprocessedspatial GPS trajectoriesusing an adaption of the Word2vec approach. Based on the embedding representation, we later train multiple classification models to achieve the inference goals.Our empirical results indicate that the suggested embedding approach outperformsaclassification approach which does not takeinto consideration the embedding patterns.Experiments on real datasets collected from Android devices show that the proposed method achieves over 80% accuracy for variousdemographic prediction tasks.