Massive user check-in histories provide valuable opportunities to understand users’ behavior and make successive point-of-interest (POI) recommendation. However, most existing works only focus on predicting the POIs that users will visit next, while ignoring when they are interested in visiting these POIs. A few works set a fixed time window to constrain the target, but still suffer form the following problems: 1) the inability to respond to interests within future dynamic time windows; 2) the inability to answer when and in what order to visit the recommended POIs. This results in them performing poorly in providing recommendations that are more beneficial to merchants and more likely to excite users. In contrast, we propose a new meaningful task, namely N-successive POI recommendation, which aims to suggest POI sequences to users that will be visited in the next N consecutive time slots. The challenge of this task lies in how to efficiently integrate interests of different granularities to maximize their value, and how to exploit consecutive interest dependencies. To this end, we propose a multi-interest granularity guided semi-joint learning model. It performs multiple combined encodings of short-term interests, long-term interests, and clock influence in a simple and effective way while learning consecutive interest dependencies. The experimental results show that our model can effectively perform N-successive POI recommendations.