研究广告竞价中模型更好为何未必带来更好收益
Model Monotonicity in Autobidding Auctions: When Do Better Predictions Lead to Better Outcomes?
- 用概率滤波思想定义模型改进，分析其对竞价结果的影响
- 发现首轮出价拍卖下，点击率预测越准，平台收入越高
- 适合广告平台优化模型与竞价机制的对齐策略
在线广告平台依赖机器学习模型预测点击率（pCTR）和转化率（pCVR）以支持竞价机制。本文提出新框架，研究推荐模型质量、竞价格式与自动出价行为之间的交互关系。我们形式化定义了基于概率滤波思想的模型改进概念，并系统分析了在不同出价类型（tCPA、max-CPA）、竞价格式（首轮、第二轮、VCG）及预算约束下的平台级评估指标（ECM）单调性。结果显示：在无预算的tCPA出价者中，统一出价的首轮出价拍卖通过Jensen不等式保证收入单调性；而第二轮出价拍卖与预算约束可能破坏该性质。我们提供了完整的数值反例构造。研究为广告平台实现模型提升与业务目标的一致性提供了实践指导。
原文摘要 · Abstract (English)
Online advertising platforms rely on machine learning models to predict click-through rates (pCTR) and conversion rates (pCVR) for auction mechanisms. We introduce a novel framework to study the interaction between recommender system model quality, auction format, and autobidder behavior. We formalize when model improvements -- defined via a refinement relation inspired by filtrations in probability theory -- lead to improvements in platform-level Evaluation Criteria Metrics (ECM) such as revenue, welfare, or liquid welfare. Our main contributions are: (1) a formal definition of model improvement based on cluster refinement, and (2) a systematic characterization of ECM monotonicity across different combinations of bidder types (tCPA, max-CPA), auction formats (first-price, second-price, VCG), and budget constraints. We show that first-price auctions with uniform bidding guarantee revenue monotonicity for tCPA bidders without budgets (via Jensen's inequality), while second-price auctions and budget constraints can break this property. We provide full numerical constructions for the non-monotonicity results. Our findings have practical implications for advertising platforms seeking to align model improvements with business outcomes.
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