the current location of the user. More importantly, while time and location together play a critical role in determining users’ activities in LBSNs, rare work has modeled their joint effects. Considering only one factor will deteriorate the predictive accuracy. For instance, a student may go to a school cafeteria or to a food court in a mall at lunch time depending on he/she is on campus or outside. It is not suggested for a system to recommend the same restaurant to a user at the same time but different location. This example shows the ineffectiveness when using one type of information but ignoring the other. However, taking both time and location into consideration exaggerates the data sparsity.

In this paper, we propose a novel spatiotemporal aware (STA) model, which captures the joint effects of spatial and temporal information. Our model has the following distinct characteristics.

-   STA takes location and time as a whole to determine the users’ choice of POIs.

-   STA embeds a spatiotemporal pair $<$time, location$>$ as *a relationship* connecting users and POIs.

By considering the time and location at the same time, our model can be successfully applied to real-time POI recommendation. Furthermore, distributed representations of 