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Foursquare

Location prediction forecasts a user’s location based on historical user mobility traces. To tackle the intrinsic sparsity issue of real-world user mobility traces, spatiotemporal contexts have been shown as significantly useful.

Data and Resources

Cite this as

Jinze Wang, Lu Zhang, Zhu Sun, Yew-Soon Ong (2024). Dataset: Foursquare. https://doi.org/10.57702/r8aet5m6

DOI retrieved: December 16, 2024

Additional Info

Field Value
Created December 16, 2024
Last update December 16, 2024
Defined In https://doi.org/10.48550/arXiv.1704.08853
Citation
  • https://doi.org/10.48550/arXiv.2308.09309
  • https://doi.org/10.48550/arXiv.2402.16310
  • https://doi.org/10.1145/3109859.3109882
  • https://doi.org/10.1109/ACCESS.2021.3076809
Author Jinze Wang
More Authors
Lu Zhang
Zhu Sun
Yew-Soon Ong
Homepage https://foursquare.com/