Massive-STEPS: Massive Semantic Trajectories for Understanding POI Check-ins -- Dataset and Benchmarks

• URL: http://arxiv.org/abs/2505.11239v2
• Date: Mon, 19 May 2025 01:17:11 GMT
• Title: Massive-STEPS: Massive Semantic Trajectories for Understanding POI Check-ins -- Dataset and Benchmarks
• Authors: Wilson Wongso, Hao Xue, Flora D. Salim,
• Abstract summary: We present Massive-STEPS (Massive Semantic Trajectories for Understanding POI Check-ins)<n>Massive-STEPS is a large-scale, publicly available benchmark dataset built upon the Semantic Trails dataset.<n>Massive-STEPS spans 12 geographically diverse cities and features more recent and culturally diverse models than prior datasets.
• Score: 8.789624590579903
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Understanding human mobility through Point-of-Interest (POI) recommendation is increasingly important for applications such as urban planning, personalized services, and generative agent simulation. However, progress in this field is hindered by two key challenges: the over-reliance on older datasets from 2012-2013 and the lack of reproducible, city-level check-in datasets that reflect diverse global regions. To address these gaps, we present Massive-STEPS (Massive Semantic Trajectories for Understanding POI Check-ins), a large-scale, publicly available benchmark dataset built upon the Semantic Trails dataset and enriched with semantic POI metadata. Massive-STEPS spans 12 geographically and culturally diverse cities and features more recent (2017-2018) and longer-duration (24 months) check-in data than prior datasets. We benchmarked a wide range of POI recommendation models on Massive-STEPS using both supervised and zero-shot approaches, and evaluated their performance across multiple urban contexts. By releasing Massive-STEPS, we aim to facilitate reproducible and equitable research in human mobility and POI recommendation. The dataset and benchmarking code are available at: https://github.com/cruiseresearchgroup/Massive-STEPS

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