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Aiming at helping users locally discovery retail services (e.g.,entertainment and dinning), Online to Offline (O2O) service platforms havebecome popular in recent years, which greatly challenge current recommendersystems. With the real data in Alipay, a feeds-like scenario for O2O services,we find that recurrence based temporal patterns and position biases commonlyexist in our scenarios, which seriously threaten the recommendationeffectiveness. To this end, we propose COUPA, an industrial system targetingfor characterizing user preference with following two considerations: (1) Timeaware preference: we employ the continuous time aware point process equippedwith an attention mechanism to fully capture temporal patterns forrecommendation. (2) Position aware preference: a position selector componentequipped with a position personalization module is elaborately designed tomitigate position bias in a personalized manner. Finally, we carefullyimplement and deploy COUPA on Alipay with a cooperation of edge, streaming andbatch computing, as well as a two-stage online serving mode, to support severalpopular recommendation scenarios. We conduct extensive experiments todemonstrate that COUPA consistently achieves superior performance and haspotential to provide intuitive evidences for recommendation

COUPA: An Industrial Recommender System for Online to Offline Service Platforms