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.

在线到离线推荐与用户和服务的时空信息密切相关，因此需要更高程度的模型个性化。传统方法基于通过收集的集中数据进行训练的统一模型结构，不太可能捕捉到不同地理区域或时间段的所有用户模式。为了解决这个挑战，我们提出了一种称为 GeoGrouse 的地理群组特定建模方法，同时研究用户偏好的通用知识和群组特定知识。我们采用自动分组范例并基于用户的地理分组指标进行验证。通过离线和在线实验验证了我们的方法的有效性，并实现了实质性的业务改进。