  The task of next POI recommendation has been studied extensively in recent
years. However, developing an unified recommendation framework to incorporate
multiple factors associated with both POIs and users remains challenging,
because of the heterogeneity nature of these information. Further, effective
mechanisms to handle cold-start and endow the system with interpretability are
also difficult topics. Inspired by the recent success of neural networks in
many areas, in this paper, we present a simple but effective neural network
framework for next POI recommendation, named NEXT. NEXT is an unified framework
to learn the hidden intent regarding user's next move, by incorporating
different factors in an unified manner. Specifically, in NEXT, we incorporate
meta-data information and two kinds of temporal contexts (i.e., time interval
and visit time). To leverage sequential relations and geographical influence,
we propose to adopt DeepWalk, a network representation learning technique, to
encode such knowledge. We evaluate the effectiveness of NEXT against
state-of-the-art alternatives and neural networks based solutions. Experimental
results over three publicly available datasets demonstrate that NEXT
significantly outperforms baselines in real-time next POI recommendation.
Further experiments demonstrate the superiority of NEXT in handling cold-start.
More importantly, we show that NEXT provides meaningful explanation of the
dimensions in hidden intent space.
