Abstract: Trajectory-user linking (TUL) refers to determining the user to whom a target trajectory belongs and has become an important  trajectory data mining task. Although deep learning-based models have made significant progress in TUL research, existing approaches mainly focus on the basic spatiotemporal features of individual trajectory points, neglecting the global spatial correlation, contextual information, and users multi-periodic movement patterns, resulting in low accuracy in TUL results. In this regard, a trajectory-user linking model based on contextual global spatial graph (CGSG-TUL) is proposed. In terms of location embedding, a contextual global spatial graph is constructed based on historical trajectories, incorporating contextual information such as proximity relationships and categories of all locations. This effectively models the spatial correlations of locations. Regarding time encoding, the timestamps of check-ins are encoded according to different time scales to capture users multi-periodic movement patterns. Experimental results on two real datasets, Foursquare-NYK and Foursquare-TKY, demonstrate that CGSG-TUL outperforms the state-of-the-art baseline model GNNTUL, with an average improvement of 2.50% and 2.72% in terms of ACC@1 and Macro-F1.

Key words: trajectory-user linking (TUL), contextual global spatial graph, multi-periodic movement pattern, graph neural network, Transformer