A Multiview Trajectory Representation Learning Method for User Identification

Le résumé fourni par la source

With the development of location-based social networks, more and more users post their check-ins on social sites. Matching accounts on different social sites based on users’ spatiotemporal trajectories has become a hot issue in current research. Existing methods do not take into account the temporal characteristics, spatial characteristics, and sequential characteristics of the user’s trajectory at the same time, resulting in the inability to obtain accurate trajectory features. To solve this problem, we propose a new user identification approach based on the spatiotemporal characteristics of user trajectories, which consists of three parts: 1) convert trajectories into graph structures that preserve both spatial connectivity and access order, then use a graph attention network (GAT) to extract spatial representations—enabling effective modeling of users’ mobility structural patterns; 2) design a time-slice enhanced long short-term memory (LSTM) model to extract temporal periodicity and sequential features, which is robust to sparse trajectories with uneven time intervals; and 3) adopt a contrastive learning framework with a modified SimCLR to mine hard negative samples, solving the problem of positive–negative sample imbalance and enhancing the discriminability of trajectory representations. Extensive experiments on real datasets demonstrate that the proposed approach is superior to baseline methods in terms of performance and efficiency.

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