Bidding-Aware Retrieval for Multi-Stage Consistency in Online Advertising
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912525396738048 |
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| author | Liu, Bin Liu, Yunfei Xu, Ziru Zhou, Zhaoyu Kou, Zhi Yang, Yeqiu Zhu, Han Xu, Jian Zheng, Bo |
| author_facet | Liu, Bin Liu, Yunfei Xu, Ziru Zhou, Zhaoyu Kou, Zhi Yang, Yeqiu Zhu, Han Xu, Jian Zheng, Bo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_05206 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bidding-Aware Retrieval for Multi-Stage Consistency in Online Advertising Liu, Bin Liu, Yunfei Xu, Ziru Zhou, Zhaoyu Kou, Zhi Yang, Yeqiu Zhu, Han Xu, Jian Zheng, Bo Machine Learning Information Retrieval 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. |
| title | Bidding-Aware Retrieval for Multi-Stage Consistency in Online Advertising |
| topic | Machine Learning Information Retrieval |
| url | https://arxiv.org/abs/2508.05206 |