  User behavior modeling is important for industrial applications such as
demographic attribute prediction, content recommendation, and target
advertising. Existing methods represent behavior log as a sequence of adopted
items and find sequential patterns; however, concrete location and time
information in the behavior log, reflecting dynamic and periodic patterns,
joint with the spatial dimension, can be useful for modeling users and
predicting their characteristics. In this work, we propose a novel model based
on graph neural networks for learning user representations from spatiotemporal
behavior data. A behavior log comprises a sequence of sessions; and a session
has a location, start time, end time, and a sequence of adopted items. Our
model's architecture incorporates two networked structures. One is a tripartite
network of items, sessions, and locations. The other is a hierarchical calendar
network of hour, week, and weekday nodes. It first aggregates embeddings of
location and items into session embeddings via the tripartite network, and then
generates user embeddings from the session embeddings via the calendar
structure. The user embeddings preserve spatial patterns and temporal patterns
of a variety of periodicity (e.g., hourly, weekly, and weekday patterns). It
adopts the attention mechanism to model complex interactions among the multiple
patterns in user behaviors. Experiments on real datasets (i.e., clicks on news
articles in a mobile app) show our approach outperforms strong baselines for
predicting missing demographic attributes.
