Knowledge-informed Bidding with Dual-process Control for Online Advertising
arXiv:2603.04920v1 Announce Type: new 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 highli
arXiv:2603.04920v1 Announce Type: new 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.
Executive Summary
This article proposes a novel method for bid optimization in online advertising, called Knowledge-informed Bidding with Dual-process control (KBD). Building on the informed machine-learning paradigm, KBD embeds human expertise as inductive biases, utilizes Decision Transformer to globally optimize multi-step bidding sequences, and implements dual-process control by combining a fast rule-based PID with DT. The method aims to address the limitations of traditional black-box machine-learning models, which fail to replicate human experts' adaptive and globally coherent decisions. Extensive experiments demonstrate KBD's advantage over existing methods, highlighting the benefits of grounding bid optimization in human expertise and dual-process control. The proposed approach has the potential to improve the efficiency and effectiveness of online advertising, particularly in data-sparse cases and out-of-distribution scenarios.
Key Points
- ▸ KBD embeds human expertise as inductive biases through the informed machine-learning paradigm.
- ▸ Decision Transformer is used to globally optimize multi-step bidding sequences.
- ▸ Dual-process control is implemented by combining a fast rule-based PID with DT.
Merits
Strength in Addressing Limitations
KBD effectively addresses the limitations of traditional black-box machine-learning models, including poor generalization in data-sparse cases, short-sighted sequential decisions, and difficulty adapting in out-of-distribution scenarios.
Demerits
Potential Complexity
The implementation of dual-process control and the informed machine-learning paradigm may introduce additional complexity, potentially making the method more difficult to deploy and maintain.
Expert Commentary
The proposed method of KBD is a significant step towards addressing the limitations of traditional black-box machine learning models in bid optimization. By incorporating human expertise and dual-process control, KBD has the potential to improve the efficiency and effectiveness of online advertising. However, the added complexity of the method may pose challenges for deployment and maintenance. Further research is needed to fully understand the implications of KBD and to explore its potential applications in other domains.
Recommendations
- ✓ Future research should focus on developing more efficient and scalable methods for implementing dual-process control and informed machine learning.
- ✓ The proposed approach should be tested in real-world scenarios to further evaluate its effectiveness and potential limitations.