  Online to offline recommendation strongly correlates with the user and
service's spatiotemporal information, therefore calling for a higher degree of
model personalization. The traditional methodology is based on a uniform model
structure trained by collected centralized data, which is unlikely to capture
all user patterns over different geographical areas or time periods. To tackle
this challenge, we propose a geographical group-specific modeling method called
GeoGrouse, which simultaneously studies the common knowledge as well as
group-specific knowledge of user preferences. An automatic grouping paradigm is
employed and verified based on users' geographical grouping indicators. Offline
and online experiments are conducted to verify the effectiveness of our
approach, and substantial business improvement is achieved.
