TULAM: trajectory-user linking via attention mechanism

Recently, the application of location-based services (LBS) has become a prevalent means to provide convenience in customers' everyday lives. However, because massive volumes of location information are collected by LBS applications, users may suffer from serious privacy issues. Prior studies have shown that the identities of users can be inferred from historical anonymous trajectories, which is formulated as the trajectory-user linking (TUL) task. Although some recurrent neural network (RNN)-based models have been proposed to capture implicit movement patterns among trajectories to improve TUL performance, they cannot learn the sequential and contextual semantics within any individual trajectory completely, leaving the advantages of RNNs underutilized. We therefore propose an RNN model with an attention mechanism called TULAM to improve the accuracy of the TUL task. TULAM learns sequential relationships within individual trajectories via RNN and captures contextual semantics from trajectories via a multi-head attention mechanism. Additionally, we propose a novel location encoding method called approximate one-hot to solve the corpus shortage problem of trajectory datasets. Evaluations were conducted on real datasets from the Gowalla and Foursquare LBS platforms. The experimental results indicate that TULAM is a practical solution that achieves significant improvements over existing methods with satisfactory model complexity and convergence.