MGSAN: A Multi-granularity Self-attention Network for Next POI Recommendation

Next Point-of-Interest (POI) recommendation has become a vital research trend, helping people find interesting and attractive locations. Existing methods usually exploit the individual-level POI sequences but failed to utilize the information of collective-level POI sequences. Since collective-level POIs, like shopping malls or plazas, are common in the real world, we argue that only the individual-level POI sequences cannot represent more semantic features and cannot express complete transition patterns. To this end, we propose a novel Multi-Granularity Self-Attention Network (MGSAN) for next POI recommendation, which utilizes the multi-granularity representation and the self-attention mechanism to capture the transition patterns of individual-level and collective-level POI sequences on two different levels of granularities. Specifically, individual-level and collective-level POI sequences are first constructed and embeddings of each check-in tuple are normalized. Then, MGSAN incorporates spatio-temporal features by introducing two temporal-aware encoders and two spatial-aware encoders and learns sequential patterns with the self-attention network for two granularities. Finally, we recommended a user’s next POI with the help of two sub-tasks, i.e., the activity task to predict the next category and the auxiliary task to predict the next POI type. Extensive experiments on three real-world datasets show that MGSAN outperforms state-of-the-art methods consistently.