Contextual Generative Auction with Permutation-level Externalities for Online Advertising
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866913613792411648 |
|---|---|
| author | Zhu, Ruitao Liu, Yangsu Chen, Dagui Ma, Zhenjia Shi, Chufeng Zheng, Zhenzhe Zhang, Jie Xu, Jian Zheng, Bo Wu, Fan |
| author_facet | Zhu, Ruitao Liu, Yangsu Chen, Dagui Ma, Zhenjia Shi, Chufeng Zheng, Zhenzhe Zhang, Jie Xu, Jian Zheng, Bo Wu, Fan |
| contents | Online advertising has become a core revenue driver for the internet industry, with ad auctions playing a crucial role in ensuring platform revenue and advertiser incentives. Traditional auction mechanisms, like GSP, rely on the independent CTR assumption and fail to account for the influence of other displayed items, termed externalities. Recent advancements in learning-based auctions have enhanced the encoding of high-dimensional contextual features. However, existing methods are constrained by the "allocation-after-prediction" design paradigm, which models set-level externalities within candidate ads and fails to consider the sequential context of the final allocation, leading to suboptimal results. This paper introduces the Contextual Generative Auction (CGA), a novel framework that incorporates permutation-level externalities in multi-slot ad auctions. Built on the structure of our theoretically derived optimal solution, CGA decouples the optimization of allocation and payment. We construct an autoregressive generative model for allocation and reformulate the incentive compatibility (IC) constraint into minimizing ex-post regret that supports gradient computation, enabling end-to-end learning of the optimal payment rule. Extensive offline and online experiments demonstrate that CGA significantly enhances platform revenue and CTR compared to existing methods, while effectively approximating the optimal auction with nearly maximal revenue and minimal regret. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_11544 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Contextual Generative Auction with Permutation-level Externalities for Online Advertising Zhu, Ruitao Liu, Yangsu Chen, Dagui Ma, Zhenjia Shi, Chufeng Zheng, Zhenzhe Zhang, Jie Xu, Jian Zheng, Bo Wu, Fan Computer Science and Game Theory Online advertising has become a core revenue driver for the internet industry, with ad auctions playing a crucial role in ensuring platform revenue and advertiser incentives. Traditional auction mechanisms, like GSP, rely on the independent CTR assumption and fail to account for the influence of other displayed items, termed externalities. Recent advancements in learning-based auctions have enhanced the encoding of high-dimensional contextual features. However, existing methods are constrained by the "allocation-after-prediction" design paradigm, which models set-level externalities within candidate ads and fails to consider the sequential context of the final allocation, leading to suboptimal results. This paper introduces the Contextual Generative Auction (CGA), a novel framework that incorporates permutation-level externalities in multi-slot ad auctions. Built on the structure of our theoretically derived optimal solution, CGA decouples the optimization of allocation and payment. We construct an autoregressive generative model for allocation and reformulate the incentive compatibility (IC) constraint into minimizing ex-post regret that supports gradient computation, enabling end-to-end learning of the optimal payment rule. Extensive offline and online experiments demonstrate that CGA significantly enhances platform revenue and CTR compared to existing methods, while effectively approximating the optimal auction with nearly maximal revenue and minimal regret. |
| title | Contextual Generative Auction with Permutation-level Externalities for Online Advertising |
| topic | Computer Science and Game Theory |
| url | https://arxiv.org/abs/2412.11544 |