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Predicting visit frequencies to new places (Short Paper)

Authors Nina Wiedemann , Ye Hong , Martin Raubal



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Author Details

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

  1. Andrea Cuttone, Sune Lehmann, and Marta C González. Understanding predictability and exploration in human mobility. EPJ Data Science, 7:1-17, 2018. Google Scholar
  2. Tuan Hung Dao, Seung Ryul Jeong, and Hyunchul Ahn. A novel recommendation model of location-based advertising: Context-Aware Collaborative Filtering using GA approach. Expert Systems with Applications, 39(3):3731-3739, 2012. Google Scholar
  3. Marta C Gonzalez, Cesar A Hidalgo, and Albert-Laszlo Barabasi. Understanding individual human mobility patterns. Nature, 453(7196):779-782, 2008. Google Scholar
  4. Linrui Han. Personal Privacy Data Protection in Location Recommendation System. In Journal of Physics: Conference Series, volume 2138, page 012026, 2021. Google Scholar
  5. Ye Hong, Henry Martin, and Martin Raubal. How do you go where?: improving next location prediction by learning travel mode information using transformers. In Proceedings of the 30th International Conference on Advances in Geographic Information Systems (SIGSPATIAL '22), 2022. Google Scholar
  6. Ye Hong, Henry Martin, Yanan Xin, Dominik Bucher, Daniel J. Reck, Kay W. Axhausen, and Martin Raubal. Conserved quantities in human mobility: From locations to trips. Transportation Research Part C: Emerging Technologies, 146:103979, 2023. Google Scholar
  7. Ye Hong, Yatao Zhang, Konrad Schindler, and Martin Raubal. Context-aware multi-head self-attentional neural network model for next location prediction. arXiv preprint arXiv:2212.01953, 2022. Google Scholar
  8. Haosheng Huang, Georg Gartner, Jukka M Krisp, Martin Raubal, and Nico Van de Weghe. Location based services: ongoing evolution and research agenda. Journal of Location Based Services, 12(2):63-93, 2018. Google Scholar
  9. Defu Lian, Yong Ge, Fuzheng Zhang, Nicholas Jing Yuan, Xing Xie, Tao Zhou, and Yong Rui. Content-Aware Collaborative Filtering for Location Recommendation Based on Human Mobility Data. In 2015 IEEE International Conference on Data Mining, pages 261-270, 2015. Google Scholar
  10. Bin Liu, Yanjie Fu, Zijun Yao, and Hui Xiong. Learning geographical preferences for point-of-interest recommendation. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '13), pages 1043-1051, 2013. Google Scholar
  11. Xin Lu, Erik Wetter, Nita Bharti, Andrew J Tatem, and Linus Bengtsson. Approaching the Limit of Predictability in Human Mobility. Scientific reports, 3(1):2923, 2013. Google Scholar
  12. Henry Martin, Henrik Becker, Dominik Bucher, David Jonietz, Martin Raubal, and Kay W. Axhausen. Begleitstudie SBB Green Class - Abschlussbericht. Working Paper No. 1439, Institute for Transport Planning and Systems, ETH Zürich, 2019. Google Scholar
  13. Henry Martin, Dominik Bucher, Ye Hong, René Buffat, Christian Rupprecht, and Martin Raubal. Graph-ResNets for short-term traffic forecasts in almost unknown cities. In NeurIPS 2019 Competition and Demonstration Track, pages 153-163, 2020. Google Scholar
  14. Henry Martin, Ye Hong, Nina Wiedemann, Dominik Bucher, and Martin Raubal. Trackintel: An open-source Python library for human mobility analysis. Computers, Environment and Urban Systems, 101:101938, 2023. Google Scholar
  15. Henry Martin, Daniel Reck, Kay Axhausen, and Martin Raubal. Empirical use and impact analysis of MaaS. Technical report, ETH Zurich, 2021. Google Scholar
  16. Henry Martin, Nina Wiedemann, Daniel J Reck, and Martin Raubal. Graph-based mobility profiling. Computers, Environment and Urban Systems, 100:101910, 2023. Google Scholar
  17. Seyyed Mohammadreza Rahimi and Xin Wang. Location Recommendation Based on Periodicity of Human Activities and Location Categories. In Advances in Knowledge Discovery and Data Mining: Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD '13), pages 377-389, 2013. Google Scholar
  18. Sulis Setiowati, Teguh Bharata Adji, and Igi Ardiyanto. Context-based awareness in location recommendation system to enhance recommendation quality: A review. In 2018 International Conference on Information and Communications Technology, pages 90-95, 2018. Google Scholar
  19. Yi-Fu Tuan. Space and place: humanistic perspective. Springer, 1979. Google Scholar
  20. Nina Wiedemann, Henry Martin, and Martin Raubal. Unlocking social network analysis methods for studying human mobility. AGILE: GIScience Series, 3:19, 2022. Google Scholar
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