Deep Sequential Multi-task Modeling for Next Check-in Time and Location Prediction

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.