An Adaptive Framework of Geographical Group-Specific Network on O2O Recommendation
This is an incremental improvement for O2O recommendation systems by addressing personalization challenges related to spatiotemporal information.
The paper tackled the problem of capturing diverse user patterns in online-to-offline recommendation by proposing GeoGrouse, a geographical group-specific modeling method that studies common and group-specific knowledge, resulting in substantial business improvement.
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.