Long- and Short-term Preference Learning for Next POI Recommendation

Next POI recommendation has been studied extensively in recent years. The goal is to recommend next POI for users at specific time given users' historical check-in data. Therefore, it is crucial to model users' general taste and recent sequential behavior. Moreover, the context information such as the category and check-in time is also important to capture user preference. To this end, we propose a long- and short-term preference learning model (LSPL) considering the sequential and context information. In long-term module, we learn the contextual features of POIs and leverage attention mechanism to capture users' preference. In the short-term module, we utilize LSTM to learn the sequential behavior of users. Specifically, to better learn the different influence of location and category of POIs, we train two LSTM models for location-based sequence and category-based sequence, respectively. Then we combine the long and short-term results to recommend next POI for users. At last, we evaluate the proposed model on two real-world datasets. The experiment results demonstrate that our method outperforms the state-of-art approaches for next POI recommendation.