Knowledge-informed Bidding with Dual-process Control for Online Advertising

TL;DR

KBD (Knowledge-informed Bidding with Dual-process control) embeds human expertise as inductive biases through the informed machine-learning paradigm, uses Decision Transformer 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).

Abstract

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.

Figures (3)

• Figure 1: The architecture of the KBD method. Blue-shaded components denote black-box modules implemented via neural networks, yellow-shaded components indicate rule-based interpretable modules, and mixed-color regions represent their fusion.
• Figure 2: Case studies on the influence of $L_{\mathrm{margin}}$. As highlighted by the ellipsoidal regions, the price-volume curve is more prone to overfitting the noisy data without $L_{\mathrm{margin}}$.
• Figure 3: Robustness of IEFormer to segment number N.