User identification in cyber-physical space: a case study on mobile query logs and trajectories

TLDR: A novel framework for user identification in cyber-physical space is proposed, which consists of three key steps: modeling the location distribution of each IP address; computing the co-occurrence with an inverted index to reduce the space and time cost; and a learning-to-rank tactic to fuse user's features shared in both spaces to improve the accuracy.

Abstract: User identification across domains draws lots of research effort in recent years. Although most of existing works focus on user identification in a single space, in this paper, we first try to identify users by fusing their activities in cyber space and physical space, which helps us obtain a comprehensive understanding about users' online behaviours as well as offline visitation. Out profound insight to tackle this problem is that we can build a connection between the cyber space and the physical space with the stable location distribution of IP addresses. Thus, we propose a novel framework for user identification in cyber-physical space, which consists of three key steps: 1) modeling the location distribution of each IP address; 2) computing the co-occurrence with an inverted index to reduce the space and time cost; and 3) a learning-to-rank tactic to fuse user's features shared in both spaces to improve the accuracy. We conduct experiments to identify individual users from mobile query logs (generated in cyber space) and trajectory data (generated in physical space) to demonstrate the efficiency and effectiveness of our framework.