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Smart city article details

Title	A Survey On Graph Neural Network-Based Next Poi Recommendation For Smart Cities
ID_Doc	5261
Authors	Yu J.; Guo L.; Zhang J.; Wang G.
Year	2024
Published	Journal of Reliable Intelligent Environments, 10, 3
DOI	http://dx.doi.org/10.1007/s40860-024-00233-z
Abstract	Amid the rise of mobile technologies and Location-Based Social Networks (LBSNs), there’s an escalating demand for personalized Point-of-Interest (POI) recommendations. Especially pivotal in smart cities, these systems aim to enhance user experiences by offering location recommendations tailored to past check-ins and visited POIs. Distinguishing itself from traditional POI recommendations, the next POI approach emphasizes predicting the immediate subsequent location, factoring in both geographical attributes and temporal patterns. This approach, while promising, faces with challenges like capturing evolving user preferences and navigating data biases. The introduction of Graph Neural Networks (GNNs) brings forth a transformative solution, particularly in their ability to capture high-order dependencies between POIs, understanding deeper relationships and patterns beyond immediate connections. This survey presents a comprehensive exploration of GNN-based next POI recommendation approaches, delving into their unique characteristics, inherent challenges, and potential avenues for future research. © The Author(s) 2024.
Author Keywords	Graph neural networks (GNNs); Location-based services; Next point-of-interest (POI) recommendation; Smart city

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