Interest Networks (iNETs) for Cities: Cross-Platform Insights and Urban Behavior Explanations

TL;DR

This paper introduces iNETs, a framework for analyzing urban user interests across platforms and scales, leading to a personalized, explainable recommendation system for urban exploration.

Contribution

It compares cross-platform interest networks at multiple granularities and develops an explainable, adaptive recommendation system for urban regions.

Findings

Interest is mainly driven by proximity and venue similarity.

Coarser spatial levels show more consistent cross-platform patterns.

The recommendation system adapts to user profiles and provides natural-language explanations.

Abstract

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.