{"ID":2885124,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.05206","arxiv_id":"2508.05206","title":"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.","short_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 retr...","url_abs":"https://arxiv.org/abs/2508.05206","url_pdf":"https://arxiv.org/pdf/2508.05206v1","authors":"[\"Bin Liu\",\"Yunfei Liu\",\"Ziru Xu\",\"Zhaoyu Zhou\",\"Zhi Kou\",\"Yeqiu Yang\",\"Han Zhu\",\"Jian Xu\",\"Bo Zheng\"]","published":"2025-08-07T09:43:34Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.IR\"]","methods":"[]","has_code":false}