The next Point-Of-Interest (POI) recommendation is one of the most important services of the Location-Based Social Network (LBSN). It can not only help users find the destination which they are interested in, but also improve the potential income of business providers. Existing algorithms have employed user behavior sequences and the POI information for recommendation, but none of them fully utilize POI side information, thereby failing to ease the problems of cold start and sparse data. In light of the above analysis, this study proposed a POI recommendation system, Graph Embedding-Gated Recurrent Unit (GE-GRU). Firstly, GE-GRU relies on Graph Embedding (GE) to integrate the POI itself with its side information to get the POI embedding that contains deep information. Then, the POI embedding is input into the GRU-based neural network to model recent user preferences to acquire user embedding. Finally, according to the POI rank list, the next POI can be recommended. Experiments are conducted on a real dataset, Foursquare, which contains more than 480 000 check-ins, and Accuracy@k is adopted for evaluation. The results show that, compared with GRU and Long Short-Term Memory (LSTM), GE-GRU has 3% and 7% improvement on Accuracy@10, respectively.