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.
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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