POIFormer: A Transformer-Based Framework for Accurate and Scalable Point-of-Interest Attribution

• URL: http://arxiv.org/abs/2507.09137v1
• Date: Sat, 12 Jul 2025 04:37:52 GMT
• Title: POIFormer: A Transformer-Based Framework for Accurate and Scalable Point-of-Interest Attribution
• Authors: Nripsuta Ani Saxena, Shang-Ling Hsu, Mehul Shetty, Omar Alkhadra, Cyrus Shahabi, Abigail L. Horn,
• Abstract summary: textsfPOIFormer is a novel Transformer-based framework for accurate and efficient POI attribution.<n>textsfPOIFormer enables accurate, efficient attribution in large, noisy mobility datasets.
• Score: 3.729614737011418
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurately attributing user visits to specific Points of Interest (POIs) is a foundational task for mobility analytics, personalized services, marketing and urban planning. However, POI attribution remains challenging due to GPS inaccuracies, typically ranging from 2 to 20 meters in real-world settings, and the high spatial density of POIs in urban environments, where multiple venues can coexist within a small radius (e.g., over 50 POIs within a 100-meter radius in dense city centers). Relying on proximity is therefore often insufficient for determining which POI was actually visited. We introduce \textsf{POIFormer}, a novel Transformer-based framework for accurate and efficient POI attribution. Unlike prior approaches that rely on limited spatiotemporal, contextual, or behavioral features, \textsf{POIFormer} jointly models a rich set of signals, including spatial proximity, visit timing and duration, contextual features from POI semantics, and behavioral features from user mobility and aggregated crowd behavior patterns--using the Transformer's self-attention mechanism to jointly model complex interactions across these dimensions. By leveraging the Transformer to model a user's past and future visits (with the current visit masked) and incorporating crowd-level behavioral patterns through pre-computed KDEs, \textsf{POIFormer} enables accurate, efficient attribution in large, noisy mobility datasets. Its architecture supports generalization across diverse data sources and geographic contexts while avoiding reliance on hard-to-access or unavailable data layers, making it practical for real-world deployment. Extensive experiments on real-world mobility datasets demonstrate significant improvements over existing baselines, particularly in challenging real-world settings characterized by spatial noise and dense POI clustering.

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