用残差量化让预排序模型具备目标注意力能力，兼顾效率与效果。
Equip Pre-ranking with Target Attention by Residual Quantization
- 通过残差量化模拟目标注意力结构，提升预排序建模能力。
- 在淘宝线上测试中显著提升推荐效果，支持数千万日活用户。
- 适合追求高精度且对延迟敏感的工业级推荐系统应用。
工业推荐系统的预排序阶段面临效率与效果的根本矛盾：尽管目标注意力（TA）等强大模型在排序阶段能有效捕捉复杂特征交互，但其高计算成本使其难以用于对延迟敏感的预排序阶段，后者通常依赖简单的向量内积模型，导致整体系统性能受限。为此，我们提出TARQ——一种新颖的预排序框架。受生成模型启发，TARQ的核心创新在于通过残差量化，使预排序阶段具备近似于TA的架构能力，首次将TA的建模优势引入低延迟的预排序阶段，实现了准确率与效率的新基准。在淘宝上开展的大规模离线实验和在线A/B测试均证明TARQ显著提升了排序性能。目前该模型已全面上线生产环境，服务数千万日活跃用户，带来显著业务增长。代码与数据详见https://github.com/zyody/tarq_sigir2026。
原文摘要 · Abstract (English)
The pre-ranking stage in industrial recommendation systems faces a fundamental conflict between efficiency and effectiveness. While powerful models like Target Attention (TA) excel at capturing complex feature interactions in the ranking stage, their high computational cost makes them infeasible for pre-ranking, which often relies on simplistic vector-product models. This disparity creates a significant performance bottleneck for the entire system. To bridge this gap, we propose TARQ, a novel pre-ranking framework. Inspired by generative models, TARQ's key innovation is to equip pre-ranking with an architecture approximate to TA by Residual Quantization. This allows us to bring the modeling power of TA into the latency-critical pre-ranking stage for the first time, establishing a new state-of-the-art trade-off between accuracy and efficiency. Extensive offline experiments and large-scale online A/B tests at Taobao demonstrate TARQ's significant improvements in ranking performance. Consequently, our model has been fully deployed in production, serving tens of millions of daily active users and yielding substantial business improvements. The code and data are available at https://github.com/zyody/tarq_sigir2026.
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