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

TL;DR

This paper introduces KBD, a bid optimization method for online advertising that integrates human expertise and dual-process control, improving decision quality especially in data-sparse and out-of-distribution scenarios.

Contribution

It presents a novel approach combining human knowledge embedding, Decision Transformer, and dual-process control to enhance bid optimization performance.

Findings

KBD outperforms existing bid optimization methods.

Grounding in human expertise improves robustness.

Dual-process control enhances long-term decision coherence.

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.