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


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 abil- ity 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 con- currently 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 addi- tion, 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.
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PoI recommendation 논문
Next PoI recommendation task
Exploitation(local view)
Personalized preference graph
개인의 historical sequence 이용
Exploration(global view)
Spatial graph
Temporal graph
Preference graph
전체 PoI set으로부터 새로운 PoI recommendation
Balancing the exploitation-exploration trade-off