Rethinking Large-scale Pre-ranking System: Entire-chain Cross-domain
  Models
Jinbo Song Ruoran Huang Xinyang Wang Wei Huang Qian Yu Mingming Chen Yafei Yao Chaosheng Fan Changping Peng Zhangang Lin Jinghe Hu Jingping Shao
Industrial systems such as recommender systems and online advertising, have
been widely equipped with multi-stage architectures, which are divided into
several cascaded modules, including matching, pre-ranking, ranking and
re-ranking. As a critical bridge between matching and ranking, existing
pre-ranking approaches mainly endure sample selection bias (SSB) problem owing
to ignoring the entire-chain data dependence, resulting in sub-optimal
performances. In this paper, we rethink pre-ranking system from the perspective
of the entire sample space, and propose Entire-chain Cross-domain Models (ECM),
which leverage samples from the whole cascaded stages to effectively alleviate
SSB problem. Besides, we design a fine-grained neural structure named ECMM to
further improve the pre-ranking accuracy. Specifically, we propose a
cross-domain multi-tower neural network to comprehensively predict for each
stage result, and introduce the sub-networking routing strategy with $L0$
regularization to reduce computational costs. Evaluations on real-world
large-scale traffic logs demonstrate that our pre-ranking models outperform
SOTA methods while time consumption is maintained within an acceptable level,
which achieves better trade-off between efficiency and effectiveness.