基于空间丰富图神经网络的兴趣点关系推理
Points-of-Interest Relationship Inference with Spatial-enriched Graph Neural Networks
摘要：
作为基于位置的服务中的一个基本组成部分，推断兴趣点（POI）之间的关系对于服务提供商向企业主和客户提供良好的用户体验非常关键。现有的大多数关系推理方法都不是针对兴趣点的，因此无法捕获对兴趣点关系有巨大影响的独特空间特征。在这项工作中，我们提出PRIM来解决POI关系推理的多种关系类型。PRIM具有四个新的组件，包括加权关系图神经网络，类别分类集成，自我关注的空间上下文提取器，和距离特定的评分功能。在两个真实数据集上的大量实验表明，与最先进的基线相比，PRIM实现了最佳结果，并且对数据稀疏具有鲁棒性，适用于实际中看不见的情况。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
