  Next (or successive) point-of-interest (POI) recommendation has attracted
increasing attention in recent years. Most of the previous studies attempted to
incorporate the spatiotemporal information and sequential patterns of user
check-ins into recommendation models to predict the target user's next move.
However, none of these approaches utilized the social influence of each user's
friends. In this study, we discuss a new topic of next POI recommendation and
present a deep attentive network for social-aware next POI recommendation
called DAN-SNR. In particular, the DAN-SNR makes use of the self-attention
mechanism instead of the architecture of recurrent neural networks to model
sequential influence and social influence in a unified manner. Moreover, we
design and implement two parallel channels to capture short-term user
preference and long-term user preference as well as social influence,
respectively. By leveraging multi-head self-attention, the DAN-SNR can model
long-range dependencies between any two historical check-ins efficiently and
weigh their contributions to the next destination adaptively. Also, we carried
out a comprehensive evaluation using large-scale real-world datasets collected
from two popular location-based social networks, namely Gowalla and Brightkite.
Experimental results indicate that the DAN-SNR outperforms seven competitive
baseline approaches regarding recommendation performance and is of high
efficiency among six neural-network- and attention-based methods.
