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

Abstract

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.

arXiv commentTo appear in Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM), 2020

References in corpus (5)

• LINE: Large-scale Information Network Embedding
• KGAT: Knowledge Graph Attention Network for Recommendation
• Session-based Recommendation with Graph Neural Networks
• Learning Tree-based Deep Model for Recommender Systems
• MPE: A Mobility Pattern Embedding Model for Predicting Next Locations

Cited by in corpus (11)

• Large Language Models for Next Point-of-Interest Recommendation
• A Diffusion model for POI recommendation
• Empowering Next POI Recommendation with Multi-Relational Modeling
• Revisiting Mobility Modeling with Graph: A Graph Transformer Model for Next Point-of-Interest Recommendation
• Bi-Level Graph Structure Learning for Next POI Recommendation
• Graph-adaptive Rectified Linear Unit for Graph Neural Networks
• Urban Computing in the Era of Large Language Models
• Counterfactual Learning on Graphs: A Survey
• A Multi-Channel Next POI Recommendation Framework with Multi-Granularity Check-in Signals
• Activity-aware Human Mobility Prediction with Hierarchical Graph Attention Recurrent Network
• STGIN: Spatial-Temporal Graph Interaction Network for Large-scale POI Recommendation