Position Auctions in AI-Generated Content
We consider an extension to classic position auctions in which sponsored creatives are embedded within AI-generated content rather than shown in predefined slots. Leveraging advanced LLM technologies, it becomes viable to seamlessly integrate sponsored creatives with AI content and accurately estimate the context-aware benefits of differing insertion positions. However, this approach introduces novel challenges; substitution effects require rigorous treatment compared to standard position auction settings, where slots are independent of each other.
Authors
- Renato Paes Leme (ORCID: https://orcid.org/0000-0002-9799-5766)
- Kshipra Bhawalkar
- Santiago Balseiro (ORCID: https://orcid.org/0000-0002-0012-3292)
- Zhe Feng (ORCID: https://orcid.org/0000-0001-6036-375X)
- Vahab Mirrokni (ORCID: https://orcid.org/0000-0001-6705-5629)
- Aranyak Mehta (ORCID: https://orcid.org/0000-0002-6132-4901)
- Jieming Mao (ORCID: https://orcid.org/0000-0001-8416-0172)
- Yuan Deng
- Di Wang (ORCID: https://orcid.org/0000-0002-9109-0157)
Institutions
- Google (United States) (US)
Publication Details
- Published
- 2026-04-12
- DOI
- https://doi.org/10.1145/3774904.3792472
- Primary Topic
- Image Processing and 3D Reconstruction
- Type
- article
- Field-Weighted Citation Impact
- 0.00