Bid optimization in online advertising relies on black-box machine learning models that learn bidding decisions from historical data.
However, these approaches sometimes 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 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.