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.

翻译：在线到线下推荐与用户和服务的时空信息强相关，因此对模型个性化程度提出了更高要求。传统方法基于由收集的集中数据训练的单一模型结构，难以捕捉不同地理区域或时间段内所有用户模式。为解决这一挑战，我们提出一种名为GeoGrouse的地理群体特定建模方法，该方法同时研究用户偏好的共通知识和群体特定知识。采用基于用户地理分组指标的自动分组范式并验证其有效性。通过离线与在线实验验证了本方法的有效性，并实现了显著的业务提升。