Knowledge-informed Bidding with Dual-process Control for Online Advertising
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arXiv
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| Format: | Preprint |
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2026
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| author | Luo, Huixiang Gao, Longyu Liu, Yaqi Chen, Qianqian Huang, Pingchun Li, Tianning |
| author_facet | Luo, Huixiang Gao, Longyu Liu, Yaqi Chen, Qianqian Huang, Pingchun Li, Tianning |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_04920 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Knowledge-informed Bidding with Dual-process Control for Online Advertising Luo, Huixiang Gao, Longyu Liu, Yaqi Chen, Qianqian Huang, Pingchun Li, Tianning Artificial Intelligence 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. |
| title | Knowledge-informed Bidding with Dual-process Control for Online Advertising |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2603.04920 |