用生成模型优化广告竞价，提升平台收益与广告主回报。
Generative Large-Scale Pre-trained Models for Automated Ad Bidding Optimization
- 结合专家混合与因果变换器，实现多样竞价探索与约束优化。
- 线上实验显示GMV提升2.18%，ROI提高10.68%。
- 适用于大规模广告平台，适配复杂多变的投放目标。
现代自动竞价系统需在整体性能与多样化广告主目标及现实约束间取得平衡，反映行业动态需求。近年来，条件生成模型（如Transformer和扩散模型）可通过直接生成符合广告主偏好的出价轨迹，为传统基于马尔可夫决策过程的方法提供替代方案。然而，生成方法面临离线与在线分布差异、动作空间探索受限，以及需满足边际千次展示成本（CPM）和投资回报率（ROI）等约束的挑战。为此，我们提出GRAD（Generative Reward-driven Ad-bidding with Mixture-of-Experts），一种可扩展的自动竞价基础模型，融合动作专家混合模块以实现多样出价探索，并利用因果变换器的价值估计器进行约束感知优化。大量离线与在线实验表明，GRAD显著提升平台收入，有效应对现代广告主日益多样化的需求。此外，该模型已在美团——全球最大的在线外卖平台之一——的多个营销场景中落地，带来2.18%的总商品交易额（GMV）增长与10.68%的ROI提升。
原文摘要 · Abstract (English)
Modern auto-bidding systems are required to balance overall performance with diverse advertiser goals and real-world constraints, reflecting the dynamic and evolving needs of the industry. Recent advances in conditional generative models, such as transformers and diffusers, have enabled direct trajectory generation tailored to advertiser preferences, offering a promising alternative to traditional Markov Decision Process-based methods. However, these generative methods face significant challenges, such as the distribution shift between offline and online environments, limited exploration of the action space, and the necessity to meet constraints like marginal Cost-per-Mille (CPM) and Return on Investment (ROI). To tackle these challenges, we propose GRAD (Generative Reward-driven Ad-bidding with Mixture-of-Experts), a scalable foundation model for auto-bidding that combines an Action-Mixture-of-Experts module for diverse bidding action exploration with the Value Estimator of Causal Transformer for constraint-aware optimization. Extensive offline and online experiments demonstrate that GRAD significantly enhances platform revenue, highlighting its effectiveness in addressing the evolving and diverse requirements of modern advertisers. Furthermore, GRAD has been implemented in multiple marketing scenarios at Meituan, one of the world's largest online food delivery platforms, leading to a 2.18% increase in Gross Merchandise Value (GMV) and 10.68% increase in ROI.
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