  Understanding users' context is essential for successful recommendations,
especially for Online-to-Offline (O2O) recommendation, such as Yelp, Groupon,
and Koubei. Different from traditional recommendation where individual
preference is mostly static, O2O recommendation should be dynamic to capture
variation of users' purposes across time and location. However, precisely
inferring users' real-time contexts information, especially those implicit
ones, is extremely difficult, and it is a central challenge for O2O
recommendation. In this paper, we propose a new approach, called Mixture
Attentional Constrained Denoise AutoEncoder (MACDAE), to infer implicit
contexts and consequently, to improve the quality of real-time O2O
recommendation. In MACDAE, we first leverage the interaction among users,
items, and explicit contexts to infer users' implicit contexts, then combine
the learned implicit-context representation into an end-to-end model to make
the recommendation. MACDAE works quite well in the real system. We conducted
both offline and online evaluations of the proposed approach. Experiments on
several real-world datasets (Yelp, Dianping, and Koubei) show our approach
could achieve significant improvements over state-of-the-arts. Furthermore,
online A/B test suggests a 2.9% increase for click-through rate and 5.6%
improvement for conversion rate in real-world traffic. Our model has been
deployed in the product of "Guess You Like" recommendation in Koubei.
