Next Point-of-Interest (POI) recommendation is a longstanding problem across
the domains of Location-Based Social Networks (LBSN) and transportation. Recent
Recurrent Neural Network (RNN) based approaches learn POI-POI relationships in
a local view based on independent user visit sequences. This limits the model's
ability to directly connect and learn across users in a global view to
recommend semantically trained POIs. In this work, we propose a
Spatial-Temporal-Preference User Dimensional Graph Attention Network
(STP-UDGAT), a novel explore-exploit model that concurrently exploits
personalized user preferences and explores new POIs in global
spatial-temporal-preference (STP) neighbourhoods, while allowing users to
selectively learn from other users. In addition, we propose random walks as a
masked self-attention option to leverage the STP graphs' structures and find
new higher-order POI neighbours during exploration. Experimental results on six
real-world datasets show that our model significantly outperforms baseline and
state-of-the-art methods.

本研究使用新颖的探索 - 利用模型，提出使用 Spatial-Temporal-Preference User Dimensional Graph Attention Network (STP-UDGAT) 来同时为用户的个性化偏好和探索新的 POI 以及通过随机游走来寻找新的高阶 POI 邻居，以解决点 - of-Interest (POI) recommendation 的问题，并且在六个真实世界数据集上实验结果显示，该模型明显优于基准线和现有的方法。