Inferring Long-term User Properties based on Users’ Location History

Yutaka Matsuo, Naoaki Okazaki, Kiyoshi Izumi, Yoshiyuki Nakamura, Takuichi Nishimura, Koiti Hasida, Hideyuki Nakashima

Recent development of location technologies enables us to obtain the location history of users. paper proposes a new method to infer users’ longterm properties from their respective location histories. Counting the instances of sensor detection every user, we can obtain a sensor-user matrix. After generating features from the matrix, a machine learning approach is taken to automatically users into different categories for each user property. Inspired by information retrieval research, problem to infer user properties is reduced categorization problem. We compare weightings of several features and also propose sensor weighting. Our algorithms are evaluated using experimental location data in an office environment.