Constrained Preferential Bayesian Optimization and Its Application in Banner Ad Design

Iwai, Koki; Kumagae, Yusuke; Koyama, Yuki; Hamasaki, Masahiro; Goto, Masataka

doi:10.24963/ijcai.2025/463

Constrained Preferential Bayesian Optimization and Its Application in Banner Ad Design

Koki Iwai, Yusuke Kumagae, Yuki Koyama, Masahiro Hamasaki, Masataka Goto

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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 4155-4163.

https://doi.org/10.24963/ijcai.2025/463

Keywords

Humans and AI: HAI: Human-computer interaction
Humans and AI: HAI: Human-AI collaboration
Constraint Satisfaction and Optimization: CSO: Constraint optimization problems

Abstract

Preferential Bayesian optimization (PBO) is a variant of Bayesian optimization that observes relative preferences (e.g., pairwise comparisons) instead of direct objective values, making it especially suitable for human-in-the-loop scenarios. However, real-world optimization tasks often involve inequality constraints, which existing PBO methods have not yet addressed. To fill this gap, we propose constrained preferential Bayesian optimization (CPBO), an extension of PBO that incorporates inequality constraints for the first time. Specifically, we present a novel acquisition function for this purpose. Our technical evaluation shows that our CPBO method successfully identifies optimal solutions by focusing on exploring feasible regions. As a practical application, we also present a designer-in-the-loop system for banner ad design using CPBO, where the objective is the designer's subjective preference, and the constraint ensures a target predicted click-through rate. We conducted a user study with professional ad designers, demonstrating the potential benefits of our approach in guiding creative design under real-world constraints.

Koki Iwai, Yusuke Kumagae, Yuki Koyama, Masahiro Hamasaki, and Masataka Goto. 2025. Constrained Preferential Bayesian Optimization and Its Application in Banner Ad Design. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 4155–4163. https://doi.org/10.24963/ijcai.2025/463

@inproceedings{ijcai2025p463,
  title     = {Constrained Preferential Bayesian Optimization and Its Application in Banner Ad Design},
  author    = {Iwai, Koki and Kumagae, Yusuke and Koyama, Yuki and Hamasaki, Masahiro and Goto, Masataka},
  booktitle = {Proceedings of the Thirty-Fourth International Joint Conference on
               Artificial Intelligence, {IJCAI-25}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {James Kwok},
  pages     = {4155--4163},
  year      = {2025},
  month     = {8},
  note      = {Main Track},
  doi       = {10.24963/ijcai.2025/463},
  url       = {https://doi.org/10.24963/ijcai.2025/463},
}

TY  - CPAPER
TI  - Constrained Preferential Bayesian Optimization and Its Application in Banner Ad Design
AU  - Iwai, Koki
AU  - Kumagae, Yusuke
AU  - Koyama, Yuki
AU  - Hamasaki, Masahiro
AU  - Goto, Masataka
T2  - Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25
PB  - International Joint Conferences on Artificial Intelligence Organization
PY  - 2025
SP  - 4155
EP  - 4163
DO  - 10.24963/ijcai.2025/463
UR  - https://doi.org/10.24963/ijcai.2025/463
ER  -