Points-of-Interest Relationship Inference with Spatial-enriched Graph Neural Networks

TL;DR

This paper introduces PRIM, a novel spatial-enriched graph neural network model designed to accurately infer relationships between points-of-interest by capturing spatial and categorical features, outperforming existing methods.

Contribution

PRIM integrates a weighted relational GNN, category taxonomy, spatial context extraction, and a distance-specific scoring function for POI relationship inference.

Findings

PRIM achieves superior accuracy over state-of-the-art baselines.

PRIM is robust against data sparsity.

PRIM effectively handles unseen POI cases.

Abstract

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.