MTUL: A Novel Approach for Multi-Trajectory User Linking

Abstract

Trajectory User Linking (TUL) is the problem of identifying the user (i.e., his identity) from the trajectories generated by him. Existing works on TUL leverage a single trajectory for identifying a user. We propose a novel problem called Multi-Trajectory User Linking (MTUL), which leverages all available trajectories generated by a particular user to identify him. Thus, MTUL is essentially the generalized TUL problem. This problem has significant applications in Location-Based Services (LBSs) such as personalized route planning and point-of-interests (POI) recommendation, movement anomaly detection, etc. We provide an end-to-end solution to the MTUL problem using sequence embedding and GRU and achieve reasonable accuracy by taking into account the POI type and region information. We consider this work to be an important addition to the TUL research.