Points-of-Interest Relationship Inference with Spatial-enriched Graph Neural Networks
This work addresses a domain-specific problem for location-based service providers to improve user experience, but it is incremental as it builds on existing graph neural network techniques.
The paper tackles the problem of inferring relationships between points-of-interest (POIs) by proposing PRIM, a method that incorporates spatial characteristics, and it achieves state-of-the-art results on two real-world datasets with robustness against data sparsity.
As a fundamental component in location-based services, inferring the relationship between points-of-interests (POIs) is very critical for service providers to offer good user experience to business owners and customers. Most of the existing methods for relationship inference are not targeted at POI, thus failing to capture unique spatial characteristics that have huge effects on POI relationships. In this work we propose PRIM to tackle POI relationship inference for multiple relation types. PRIM features four novel components, including a weighted relational graph neural network, category taxonomy integration, a self-attentive spatial context extractor, and a distance-specific scoring function. Extensive experiments on two real-world datasets show that PRIM achieves the best results compared to state-of-the-art baselines and it is robust against data sparsity and is applicable to unseen cases in practice.