Predicting Future Location Categories of Users in a Large Social Platform.

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.