Next POI (Point of Interest) recommendation aims to recommend next POI for users at specific time given users’ historical check-ins. User relationship and preference information are important factors that can affect the user’s decision-making behavior on the next POI. To this end, we propose an approach for the next POI recommendation based on user relationship and preference information, called URPI-GRU (User relationship and Preference information Gated Recurrent Unit). URPI-GRU contains two modules, short-term module and long-term module. First, we construct a user relationship graph and learn user relationship vectors. And then we divide the check-ins into current preference, periodic preference and long-term preference according to the user’s check-in time. In the short-term module, the user’s periodic preference and current preference are learned through the GRU model, and they are concatenated with the user relationship vector to learn the short-term scores of POIs. In the long-term module, the user’s long-term preference is mined through the K-nearest neighbor sequences to obtain the long-term scores of the POIs. Last, we recommend the POIs based on the total score of short-term and long-term scores. Extensive experiments on two representative real-world datasets demonstrated that our model yields significant improvements over the state-of-the-art methods.