Spatial-temporal information has been proven to be of great significance for click-through rate prediction tasks in online Location-Based Services (LBS), especially in mainstream food ordering platforms such as DoorDash, Uber Eats, Meituan, and Ele.me. Modeling user spatial-temporal preferences with sequential behavior data has become a hot topic in recommendation systems and online advertising. However, most of existing methods either lack the representation of rich spatial-temporal information or only handle user behaviors with limited length, e.g. 100. In this paper, we tackle these problems by designing a new spatial-temporal modeling paradigm named Fragment and Integrate Network (FIN). FIN consists of two networks: (i) Fragment Network (FN) extracts Multiple Sub-Sequences (MSS) from lifelong sequential behavior data, and captures the specific spatial-temporal representation by modeling each MSS respectively. Here both a simplified attention and a complicated attention are adopted to balance the performance gain and resource consumption. (ii) Integrate Network (IN) builds a new integrated sequence by utilizing spatial-temporal interaction on MSS and captures the comprehensive spatial-temporal representation by modeling the integrated sequence with a complicated attention. Both public datasets and production datasets have demonstrated the accuracy and scalability of FIN. Since 2022, FIN has been fully deployed in the recommendation advertising system of Ele.me, one of the most popular online food ordering platforms in China, obtaining 5.7% improvement on Click-Through Rate (CTR) and 7.3% increase on Revenue Per Mille (RPM).

翻译：时空信息已被证明对在线位置服务（LBS）中的点击率预测任务具有重要价值，尤其是在DoorDash、Uber Eats、美团和饿了么等主流餐饮订购平台。利用序列行为数据建模用户时空偏好已成为推荐系统和在线广告领域的热点课题。然而，现有方法大多要么缺乏对丰富时空信息的表征能力，要么仅能处理有限长度（例如100个）的用户行为。本文通过设计一种名为片段与集成网络（FIN）的新型时空建模范式来解决这些问题。FIN由两个网络组成：（i）片段网络（FN）从全生命周期序列行为数据中提取多个子序列（MSS），并通过分别建模每个MSS来捕获特定的时空表示。此处同时采用了简化注意力机制与复杂注意力机制，以平衡性能提升与资源消耗。（ii）集成网络（IN）通过利用MSS上的时空交互构建新的集成序列，并采用复杂注意力机制建模该集成序列以捕获全面的时空表示。公开数据集与生产数据集均验证了FIN的准确性与可扩展性。自2022年起，FIN已全面部署于中国最受欢迎的在线餐饮订购平台之一饿了么的推荐广告系统中，实现了点击率（CTR）5.7%的提升和千次展示收入（RPM）7.3%的增长。