Bidding-Aware Retrieval for Multi-Stage Consistency in Online Advertising

Abstract: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.

Machine Learning (cs.LG); Information Retrieval (cs.IR)

arXiv:2508.05206 [cs.LG]

(or arXiv:2508.05206v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2508.05206 Focus to learn more arXiv-issued DOI via DataCite

https://doi.org/10.1145/3799682.3840091 Focus to learn more DOI(s) linking to related resources

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