  Aiming at helping users locally discovery retail services (e.g.,
entertainment and dinning), Online to Offline (O2O) service platforms have
become popular in recent years, which greatly challenge current recommender
systems. With the real data in Alipay, a feeds-like scenario for O2O services,
we find that recurrence based temporal patterns and position biases commonly
exist in our scenarios, which seriously threaten the recommendation
effectiveness. To this end, we propose COUPA, an industrial system targeting
for characterizing user preference with following two considerations: (1) Time
aware preference: we employ the continuous time aware point process equipped
with an attention mechanism to fully capture temporal patterns for
recommendation. (2) Position aware preference: a position selector component
equipped with a position personalization module is elaborately designed to
mitigate position bias in a personalized manner. Finally, we carefully
implement and deploy COUPA on Alipay with a cooperation of edge, streaming and
batch computing, as well as a two-stage online serving mode, to support several
popular recommendation scenarios. We conduct extensive experiments to
demonstrate that COUPA consistently achieves superior performance and has
potential to provide intuitive evidences for recommendation
