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

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

BibTeX Citation

@misc{wongso2025massivestepsmassivesemantictrajectories,
  title={Massive-STEPS: Massive Semantic Trajectories for Understanding POI Check-ins -- Dataset and Benchmarks}, 
  author={Wilson Wongso and Hao Xue and Flora D. Salim},
  year={2025},
  eprint={2505.11239},
  archivePrefix={arXiv},
  primaryClass={cs.LG},
  url={https://arxiv.org/abs/2505.11239}, 
}

Recommended citation: Wongso, W., Xue, H., & Salim, F. D. (2025). Massive-STEPS: Massive Semantic Trajectories for Understanding POI Check-ins--Dataset and Benchmarks. arXiv preprint arXiv:2505.11239.
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