Abstract:
Many trajectories have user ID, location ID, time-stamp, and activity type (like “topic” in text mining) due to the widespread use of positioning devices. Existing works use latent activity types from topic models or embedding techniques to model these trajectories.
This paper proposes a holistic approach called Human Mobility Representation Model (HMRM) to simultaneously produce vector representations of all four attributes (explicit and implicit).
HMRM’s strengths are that (1) it models latent activity types and learns trajectory attribute embeddings integratedly, and (2) it connects the activity-related distributions and these attribute embeddings by adding a newly designed collaborative learning component and makes them mutually exchanged to take the best of both worlds.
We use HMRM to evaluate two Foursquare check-in datasets’ activity and embedding tasks. HMRM improved trajectory embeddings and latent activity type capture in experiments.
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