A Hybrid Cross-Stage Coordination Pre-ranking Model for Online Recommendation Systems
Binglei Zhao, Houying Qi, Guang Xu, Mian Ma, Xiwei Zhao, Feng Mei, Sulong Xu, Jinghe Hu · 2025
Large-scale recommendation systems often adopt cascading architecture consisting of retrieval, pre-ranking, ranking, and re-ranking stages.With strict latency requirements, pre-ranking utilizes lightweight models to perform a preliminary selection from massive retrieved candidates.However, recent works focus solely on improving consistency with ranking, relying exclusively on downstream stages.Since downstream input is derived from the pre-ranking output, they will exacerbate the sample selection bias (SSB) issue and Matthew effect, leading to sub-optimal results.To address the limitation, we propose a novel Hybrid Cross-Stage Coordination Pre-ranking model (HCCP) to integrate information from upstream (retrieval) and downstream (ranking, re-ranking) stages.Specifically, cross-stage coordination refers to the pre-ranking's adaptability to the entire stream and the role of serving as a more effective bridge between upstream and downstream.HCCP consists of Hybrid Sample Construction and Hybrid Objective Optimization.Hybrid sample construction captures multi-level unexposed data from the entire stream and rearranges them to become the optimal guiding "ground truth" for pre-ranking learning.Hybrid objective optimization contains the joint optimization of consistency and long-tail precision through our proposed Margin InfoNCE loss.It is specifically designed to learn from such hybrid unexposed samples, improving the overall performance and mitigating the SSB issue.The appendix describes a proof of the efficacy of the proposed loss in selecting potential positives.Extensive offline and online experiments indicate that HCCP outperforms SOTA methods by improving cross-stage coordination.It contributes up to 14.9%