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Next Point-of-Interest (POI) recommendation is a longstanding problem acrossthe domains of Location-Based Social Networks (LBSN) and transportation. RecentRecurrent Neural Network (RNN) based approaches learn POI-POI relationships ina local view based on independent user visit sequences. This limits the model'sability to directly connect and learn across users in a global view torecommend semantically trained POIs. In this work, we propose aSpatial-Temporal-Preference User Dimensional Graph Attention Network(STP-UDGAT), a novel explore-exploit model that concurrently exploitspersonalized user preferences and explores new POIs in globalspatial-temporal-preference (STP) neighbourhoods, while allowing users toselectively learn from other users. In addition, we propose random walks as amasked self-attention option to leverage the STP graphs' structures and findnew higher-order POI neighbours during exploration. Experimental results on sixreal-world datasets show that our model significantly outperforms baseline andstate-of-the-art methods.

STP-UDGAT: Spatial-Temporal-Preference User Dimensional Graph Attention Network for Next POI Recommendation