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

翻译：摘要：为帮助用户发现本地零售服务（如娱乐和餐饮），线上到线下（O2O）服务平台近年来日益普及，这对现有推荐系统构成了巨大挑战。基于支付宝中O2O服务的类信息流场景的真实数据，我们发现在此类场景中普遍存在基于重复性的时间模式与位置偏差，严重威胁推荐有效性。为此，我们提出工业级系统COUPA，旨在通过以下两点刻画用户偏好：（1）时间感知偏好：采用配备注意力机制的连续时间感知点过程，充分捕获推荐中的时间模式；（2）位置感知偏好：精心设计集成位置个性化模块的位置选择器组件，以个性化方式缓解位置偏差。最后，我们通过边缘计算、流计算与批处理的协同，以及两阶段在线服务模式，在支付宝上仔细实现并部署COUPA，以支持多个热门推荐场景。通过大量实验证明，COUPA始终能取得优越性能，并具备为推荐提供直观证据的潜力。