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

• URL: http://arxiv.org/abs/2508.05206v1
• Date: Thu, 07 Aug 2025 09:43:34 GMT
• Title: Bidding-Aware Retrieval for Multi-Stage Consistency in Online Advertising
• Authors: Bin Liu, Yunfei Liu, Ziru Xu, Zhaoyu Zhou, Zhi Kou, Yeqiu Yang, Han Zhu, Jian Xu, Bo Zheng,
• Abstract summary: Bidding-Aware Retrieval (BAR) is a model-based retrieval framework that addresses multi-stage inconsistency by incorporating ad bid value into the retrieval scoring function.<n>BAR's core innovation is Bidding-Aware Modeling, incorporating bid signals through monotonicity-constrained learning and multi-task distillation to ensure economically coherent representations.<n>Extensive offline experiments and full-scale deployment across Alibaba's display advertising platform validated BAR's efficacy.
• Score: 30.108437268612438
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Multi-task Offline Reinforcement Learning for Online Advertising in Recommender Systems [54.709976343045824]
  Current offline reinforcement learning (RL) methods face substantial challenges when applied to sparse advertising scenarios.<n>We propose MTORL, a novel multi-task offline RL model that targets two key objectives.<n>We employ multi-task learning to decode actions and rewards, simultaneously addressing channel recommendation and budget allocation.
  arXiv  Detail & Related papers  (2025-06-29T05:05:13Z)
• BAT: Benchmark for Auto-bidding Task [67.56067222427946]
  We present an auction benchmark encompassing the two most prevalent auction formats.<n>We implement a series of robust baselines on a novel dataset.<n>This benchmark provides a user-friendly and intuitive framework for researchers and practitioners to develop and refine innovative autobidding algorithms.
  arXiv  Detail & Related papers  (2025-05-13T12:12:34Z)
• HCMRM: A High-Consistency Multimodal Relevance Model for Search Ads [10.61722566941537]
  This paper focuses on improving query-to-video relevance matching to enhance the effectiveness of ranking in ad systems.<n>It utilizes a simple yet effective method to enhance the consistency between pre-training and relevance tasks.<n>The proposed method has been deployed in the Kuaishou search advertising system for over a year, contributing to a 6.1% reduction in the proportion of irrelevant ads and a 1.4% increase in ad revenue.
  arXiv  Detail & Related papers  (2025-02-09T09:07:11Z)
• Know in AdVance: Linear-Complexity Forecasting of Ad Campaign Performance with Evolving User Interest [31.40023913908308]
  Real-time Bidding (RTB) advertisers wish to textitknow in advance the expected cost and yield of ad campaigns to avoid trial-and-error expenses. We propose textitAdVance, a time-aware framework that integrates local auction-level and global campaign-level modeling. AdVance has been deployed on the Tencent Advertising platform, and A/B tests show a remarkable 4.5% uplift in Average Revenue per User (ARPU)
  arXiv  Detail & Related papers  (2024-05-17T10:22:36Z)
• Online Ad Procurement in Non-stationary Autobidding Worlds [10.871587311621974]
  We introduce a primal-dual algorithm for online decision making with multi-dimension decision variables, bandit feedback and long-term uncertain constraints. We show that our algorithm achieves low regret in many worlds when procurement outcomes are generated through procedures that are adversarial, adversarially corrupted, periodic, and ergodic.
  arXiv  Detail & Related papers  (2023-07-10T00:41:08Z)
• Demystifying Advertising Campaign Bid Recommendation: A Constraint target CPA Goal Optimization [19.857681941728597]
  This paper presents a bid optimization scenario to achieve the desired cost-per-acquisition (tCPA) goals for advertisers. We build the optimization engine to make a decision by solving the rigorously formalized constrained optimization problem. The proposed model can naturally recommend the bid that meets the advertisers' expectations by making inference over advertisers' historical auction behaviors.
  arXiv  Detail & Related papers  (2022-12-26T07:43:26Z)
• VFed-SSD: Towards Practical Vertical Federated Advertising [53.08038962443853]
  We propose a semi-supervised split distillation framework VFed-SSD to alleviate the two limitations. Specifically, we develop a self-supervised task MatchedPair Detection (MPD) to exploit the vertically partitioned unlabeled data. Our framework provides an efficient federation-enhanced solution for real-time display advertising with minimal deploying cost and significant performance lift.
  arXiv  Detail & Related papers  (2022-05-31T17:45:30Z)
• A Cooperative-Competitive Multi-Agent Framework for Auto-bidding in Online Advertising [53.636153252400945]
  We propose a general Multi-Agent reinforcement learning framework for Auto-Bidding, namely MAAB, to learn the auto-bidding strategies. Our approach outperforms several baseline methods in terms of social welfare and guarantees the ad platform's revenue.
  arXiv  Detail & Related papers  (2021-06-11T08:07:14Z)
• Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential Advertising [52.3825928886714]
  We formulate the sequential advertising strategy optimization as a dynamic knapsack problem. We propose a theoretically guaranteed bilevel optimization framework, which significantly reduces the solution space of the original optimization space. To improve the exploration efficiency of reinforcement learning, we also devise an effective action space reduction approach.
  arXiv  Detail & Related papers  (2020-06-29T18:50:35Z)
• MoTiAC: Multi-Objective Actor-Critics for Real-Time Bidding [47.555870679348416]
  We propose a Multi-ecTive Actor-Critics algorithm named MoTiAC for the problem of bidding optimization with various goals. Unlike previous RL models, the proposed MoTiAC can simultaneously fulfill multi-objective tasks in complicated bidding environments.
  arXiv  Detail & Related papers  (2020-02-18T07:16:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.