提出HOB策略，在多渠道竞价中实现成本均衡，提升广告收益。
HOB: A Holistically Optimized Bidding Strategy under Heterogeneous Bidding Environments
- 通过统一边际成本计算，适配不同竞价机制
- 在线实验提升GMV 3.0%，维持广告投入回报率
- 适合需要跨渠道优化的广告平台和投放系统
在异构广告渠道中优化单一广告活动是工业级自动竞价的核心挑战。各渠道在排名规则（纯eCPM vs. UE增强评分）、定价格式（首价制 vs. 次价制）和出价惯例（统一出价 vs. 非统一出价）上存在差异，且广告主需满足全局预算约束。本文提出HOB，首次在首价制与自然-付费共存场景下实现可计算、可对齐的边际成本（MC）。HOB在全局层面推导各渠道特定的MC形式，并通过共享MC目标协调异构渠道；在局部层面，采用零膨胀指数分布建模免费赢取概率与中标价格不确定性，得到非统一首价制下的高效盈余最优出价策略。实验表明，任意内点最优解均满足跨渠道MC相等。在受控离线基准、工业日志回放及大规模在线A/B测试中，HOB持续带来显著性能提升。部署于大型商业DSP后，实现GMV提升3.0%的同时，严格满足ROAS约束。
原文摘要 · Abstract (English)
Optimizing a single advertising campaign across heterogeneous channels is a central challenge in industrial autobidding. Auction mechanisms vary across channels in ranking rules (pure eCPM vs. UE-augmented scoring), pricing formats (first- vs. second-price), and bidding conventions (uniform vs. non-uniform), while advertisers impose shared campaign-level constraints. We propose HOB, which makes marginal cost (MC) computable and alignable across heterogeneous channels, especially for first-price auctions (FPA) with organic-paid coexistence, where existing bidding formulations do not yield a practical aligned MC form. At the global level, HOB derives channel-specific MC forms and coordinates disparate channels through a shared MC target. At the local level, HOB models free-win probability and winning-price uncertainty with a zero-inflated exponential distribution, yielding an efficient surplus-optimal bidding strategy for non-uniform first-price auctions. We show that any interior optimum satisfies MC equalization across channels. Experiments on a controlled offline benchmark, industrial log replay, and large-scale online A/B tests demonstrate that HOB consistently delivers significant performance gains. Deployed on a large-scale commercial DSP, HOB delivers a 3.0% lift in GMV while maintaining return on advertising spend (ROAS) constraints.
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