通过跨平台兴趣网络分析城市行为，实现可解释的个性化推荐
Interest Networks (iNETs) for Cities: Cross-Platform Insights and Urban Behavior Explanations
- 构建多尺度兴趣网络，融合地理与场所相似性建模用户兴趣分布
- 发现粗粒度下跨平台规律一致，细粒度暴露平台特异性行为差异
- 支持探索者与回归者等不同用户类型，提供自然语言解释推荐理由
基于位置的社交网络（LBSNs）为建模城市行为提供了丰富基础，本文提出兴趣网络（iNETs）来捕捉用户兴趣在城市空间中的分布。研究对比了Google Places与Foursquare两个平台在不同空间粒度下的iNETs，发现粗粒度层级能揭示更一致的跨平台模式，而细粒度则暴露平台特有的行为特征。分析表明，用户兴趣主要受地理邻近性和场所相似性影响，社会经济与政治背景作用较小。基于此，我们开发了一个多层次、可解释的推荐系统，能够预测不同用户类型（如依赖邻近性的探索者、偏好熟悉场所的回归者）的高兴趣区域，并结合可解释AI（XAI）技术生成自然语言解释。为支持该方法，我们引入h3-cities工具用于多尺度空间分析，并发布公开演示系统以交互式探索个性化城市推荐。研究成果为城市出行研究提供了可扩展、上下文感知且可解释的推荐系统。
原文摘要 · Abstract (English)
Location-Based Social Networks (LBSNs) provide a rich foundation for modeling urban behavior through iNETs (Interest Networks), which capture how user interests are distributed throughout urban spaces. This study compares iNETs across platforms (Google Places and Foursquare) and spatial granularities, showing that coarser levels reveal more consistent cross-platform patterns, while finer granularities expose subtle, platform-specific behaviors. Our analysis finds that, in general, user interest is primarily shaped by geographic proximity and venue similarity, while socioeconomic and political contexts play a lesser role. Building on these insights, we develop a multi-level, explainable recommendation system that predicts high-interest urban regions for different user types. The model adapts to behavior profiles -- such as explorers, who are driven by proximity, and returners, who prefer familiar venues -- and provides natural-language explanations using explainable AI (XAI) techniques. To support our approach, we introduce h3-cities, a tool for multi-scale spatial analysis, and release a public demo for interactively exploring personalized urban recommendations. Our findings contribute to urban mobility research by providing scalable, context-aware, and interpretable recommendation systems.
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