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Multi-task Adversarial Learning for Semi-supervised Trajectory-User Linking

Trajectory-User Linking (TUL), which aims to link the trajectories to the users who have generated them, is critically important to many real applications. Existing approaches generally consider TUL as a supervised learning problem which requires a large number of labeled trajectory-user pairs. However, in real scenarios users may not be willing to make their identities publicly available due to data privacy concerns, leading to the scarcity of labeled trajectory-user pairs. In addition, the trajectory data are usually sparse as users will not always check-in when they go to POIs. To address these issues, in this paper we propose a multi-task adversarial learning model named TULMAL for semi-supervised TUL with spare trajectory data. Specifically, TULMAL first conducts sparse trajectory completion through a proposed seq2seq model. Kalman filter is also coupled into the decoder of the seq2seq model to calibrate the generated new locations. The completed trajectories are next input into a generative adversarial learning model for semi-supervised TUL. The insight is that we consider all the users and their trajectories as a whole and perform TUL in the data distribution level. We first project users and trajectories into the common latent feature space through learning a projection function (generator) to minimize the distance between the user distribution and the trajectory distribution. Then each unlabeled trajectory will be linked to the user who is closest to it in the latent feature space without much guidance of labels. The two tasks are jointly conducted and optimized under a multi-task learning framework. Extensive experimental results on two real-world trajectory datasets demonstrate the superiority of our proposal by comparison with existing approaches.

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