Abstract
In this paper, we address the problem of next check-in time and location prediction, and propose a deep sequential multi-task model, named Personalized Recurrent Point Process with Attention (PRPPA), which seamlessly integrates user static representation learning, dynamic recent check-in behavior modeling, and temporal point process into a unified architecture. An attention mechanism is further included in the intensity function of point process to enhance the capability of explicitly capturing the effect of past check-in events. Through the experiments, we verify the proposed model is effective in location and time prediction.
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This work was supported in part by Shanghai Sailing Program (17YF1404500), SHMEC (16CG24), and NSFC (61702190, U1609220).
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Liang, W., Zhang, W., Wang, X. (2019). Deep Sequential Multi-task Modeling for Next Check-in Time and Location Prediction. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds) Database Systems for Advanced Applications. DASFAA 2019. Lecture Notes in Computer Science(), vol 11448. Springer, Cham. https://doi.org/10.1007/978-3-030-18590-9_44
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DOI: https://doi.org/10.1007/978-3-030-18590-9_44
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