Fragment and Integrate Network (FIN): A Novel Spatial-Temporal Modeling
  Based on Long Sequential Behavior for Online Food Ordering Click-Through Rate
  Prediction
Jun Li Jingjian Wang Hongwei Wang Xing Deng Jielong Chen Bing Cao Zekun Wang Guanjie Xu Ge Zhang Feng Shi Hualei Liu
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).