  Next Point-of-Interest (POI) recommendation is of great value for both
location-based service providers and users. Recently Recurrent Neural Networks
(RNNs) have been proved to be effective on sequential recommendation tasks.
However, existing RNN solutions rarely consider the spatio-temporal intervals
between neighbor check-ins, which are essential for modeling user check-in
behaviors in next POI recommendation. In this paper, we propose a new variant
of LSTM, named STLSTM, which implements time gates and distance gates into LSTM
to capture the spatio-temporal relation between successive check-ins.
Specifically, one-time gate and one distance gate are designed to control
short-term interest update, and another time gate and distance gate are
designed to control long-term interest update. Furthermore, to reduce the
number of parameters and improve efficiency, we further integrate coupled input
and forget gates with our proposed model. Finally, we evaluate the proposed
model using four real-world datasets from various location-based social
networks. Our experimental results show that our model significantly
outperforms the state-of-the-art approaches for next POI recommendation.
