Predicting Future Location Categories of Users in a Large Social Platform


  • Raiyan Abdul Baten University of Rochester
  • Yozen Liu Snap Inc.
  • Heinrich Peters Columbia University in the City of New York
  • Francesco Barbieri Snap Inc.
  • Neil Shah Snap Inc.
  • Leonardo Neves Snap Inc.
  • Maarten W. Bos Snap Inc.



Web and Social Media, Social media usage on mobile devices; location, human mobility, and behavior, Trend identification and tracking; time series forecasting, Qualitative and quantitative studies of social media


Understanding the users' patterns of visiting various location categories can help online platforms improve content personalization and user experiences. Current literature on predicting future location categories of a user typically employs features that can be traced back to the user, such as spatial geo-coordinates and demographic identities. Moreover, existing approaches commonly suffer from cold-start and generalization problems, and often cannot specify when the user will visit the predicted location category. In a large social platform, it is desirable for prediction models to avoid using user-identifiable data, generalize to unseen and new users, and be able to make predictions for specific times in the future. In this work, we construct a neural model, LocHabits, using data from Snapchat. The model omits user-identifiable inputs, leverages temporal and sequential regularities in the location category histories of Snapchat users and their friends, and predicts the users' next-hour location categories. We evaluate our model on several real-life, large-scale datasets from Snapchat and FourSquare, and find that the model can outperform baselines by 14.94% accuracy. We confirm that the model can (1) generalize to unseen users from different areas and times, and (2) fall back on collective trends in the cold-start scenario. We also study the relative contributions of various factors in making the predictions and find that the users' visitation preferences and most-recent visitation sequences play more important roles than time contexts, same-hour sequences, and social influence features.




How to Cite

Baten, R. A., Liu, Y., Peters, H., Barbieri, F., Shah, N., Neves, L., & Bos, M. W. (2023). Predicting Future Location Categories of Users in a Large Social Platform. Proceedings of the International AAAI Conference on Web and Social Media, 17(1), 47-58.