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Predicting visit frequencies to new places (Short Paper)

Authors Nina Wiedemann , Ye Hong , Martin Raubal

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Nina Wiedemann
  • Institute of Cartography and Geoinformation, ETH Zürich, Switzerland
Ye Hong
  • Institute of Cartography and Geoinformation, ETH Zürich, Switzerland
Martin Raubal
  • Institute of Cartography and Geoinformation, ETH Zürich, Switzerland

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Nina Wiedemann, Ye Hong, and Martin Raubal. Predicting visit frequencies to new places (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 84:1-84:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023) https://doi.org/10.4230/LIPIcs.GIScience.2023.84

Abstract

Human mobility exhibits power-law distributed visitation patterns; i.e., a few locations are visited frequently and many locations only once. Current research focuses on the important locations of users or on recommending new places based on collective behaviour, neglecting the existence of scarcely visited locations. However, assessing whether a user will return to a location in the future is highly relevant for personalized location-based services. Therefore, we propose a new problem formulation aimed at predicting the future visit frequency to a new location, focusing on the previous mobility behaviour of a single user. Our preliminary results demonstrate that visit frequency prediction is a difficult task, but sophisticated learning models can detect insightful patterns in the historic mobility indicative of future visit frequency. We believe these models can uncover valuable insights into the spatial factors that drive individual mobility behaviour.

Subject Classification

ACM Subject Classification
  • Information systems → Geographic information systems
  • Computing methodologies → Neural networks
  • Applied computing → Transportation
  • Information systems → Location based services
Keywords
  • Human mobility
  • Visitation patterns
  • Place recommendation
  • Next location prediction

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References

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