让检索阶段读懂广告出价，提升平台收入和广告效果。
Bidding-Aware Retrieval for Multi-Stage Consistency in Online Advertising
- 引入出价信息重构检索打分，解决多阶段不一致问题。
- 平台收入增4.32%，优质广告曝光量升22.2%。
- 适合大规模在线广告系统优化与自动出价场景。
在线广告系统普遍采用级联架构应对海量请求与候选集，各排序阶段依据eCPM（预估点击率×出价）分配流量。随着自动出价策略普及，检索阶段因无法获取真实、实时的广告出价，与排序阶段的不一致性加剧，导致平台收益与广告主效果下降。为此，我们提出Bidding-Aware Retrieval（BAR）模型框架，通过在检索打分中融入出价信息，缓解多阶段不一致问题。核心创新包括：基于单调性约束学习与多任务蒸馏的出价感知建模，确保经济一致性表示；异步近线推理实现嵌入向量实时更新以提升市场响应能力；任务注意力精炼模块可分离用户兴趣与商业价值信号。在阿里巴巴展示广告平台的离线实验与全量部署验证了其有效性：平台收入提升4.32%，正向运营广告的展示量增加22.2%。
原文摘要 · Abstract (English)
Online advertising systems typically use a cascaded architecture to manage massive requests and candidate volumes, where the ranking stages allocate traffic based on eCPM (predicted CTR $\times$ Bid). With the increasing popularity of auto-bidding strategies, the inconsistency between the computationally sensitive retrieval stage and the ranking stages becomes more pronounced, as the former cannot access precise, real-time bids for the vast ad corpus. This discrepancy leads to sub-optimal platform revenue and advertiser outcomes. To tackle this problem, we propose Bidding-Aware Retrieval (BAR), a model-based retrieval framework that addresses multi-stage inconsistency by incorporating ad bid value into the retrieval scoring function. The core innovation is Bidding-Aware Modeling, incorporating bid signals through monotonicity-constrained learning and multi-task distillation to ensure economically coherent representations, while Asynchronous Near-Line Inference enables real-time updates to the embedding for market responsiveness. Furthermore, the Task-Attentive Refinement module selectively enhances feature interactions to disentangle user interest and commercial value signals. Extensive offline experiments and full-scale deployment across Alibaba's display advertising platform validated BAR's efficacy: 4.32% platform revenue increase with 22.2% impression lift for positively-operated advertisements.
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