用人类经验指导广告竞价，提升决策适应性和长期效果
Knowledge-informed Bidding with Dual-process Control for Online Advertising
- 融合人类经验作为先验知识，用双系统控制优化竞价策略
- 在数据稀疏和分布外场景下，竞标表现优于传统模型
- 适合需要长期稳定竞价的广告投放场景
在线广告中的出价优化依赖于从历史数据中学习的黑箱机器学习模型，但这些方法难以复现人类专家的自适应、经验驱动和全局一致的决策。具体表现为：在数据稀疏情况下因缺乏结构化知识而泛化能力差；做出短视的序列决策，忽略长期相互依赖关系；在分布外场景下难以适应，而人类专家却能应对。为此，我们提出KBD（Knowledge-informed Bidding with Dual-process control），一种新型出价优化方法。KBD通过有监督机器学习范式将人类专业知识嵌入为归纳偏置，使用决策变换器（Decision Transformer, DT）全局优化多步出价序列，并通过结合快速规则型PID（系统1）与DT（系统2）实现双过程控制。大量实验表明，KBD在性能上优于现有方法，验证了将出价优化建立在人类经验与双过程控制基础上的有效性。
原文摘要 · Abstract (English)
Bid optimization in online advertising relies on black-box machine-learning models that learn bidding decisions from historical data. However, these approaches fail to replicate human experts' adaptive, experience-driven, and globally coherent decisions. Specifically, they generalize poorly in data-sparse cases because of missing structured knowledge, make short-sighted sequential decisions that ignore long-term interdependencies, and struggle to adapt in out-of-distribution scenarios where human experts succeed. To address this, we propose KBD (Knowledge-informed Bidding with Dual-process control), a novel method for bid optimization. KBD embeds human expertise as inductive biases through the informed machine-learning paradigm, uses Decision Transformer (DT) to globally optimize multi-step bidding sequences, and implements dual-process control by combining a fast rule-based PID (System 1) with DT (System 2). Extensive experiments highlight KBD's advantage over existing methods and underscore the benefit of grounding bid optimization in human expertise and dual-process control.
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