Joint Learning User's Activities and Profiles From GPS Data
As the GPS-enabled mobile devices become extensively available, the authors are now given a chance to better understand human behaviors from a large amount of the GPS trajectories representing the mobile users' location histories. In this paper, they aim to establish a framework, which can jointly learn the user activities (what is the user doing) and profiles (what is the user's background, such as occupation, gender, age, etc.) from the GPS data. They will show that, learning user activities and learning user profiles can be beneficial to each other in nature, so they try to put them together and formulate a joint learning problem under a probabilistic collaborative filtering framework.