2025/05/23 by Ze Wang, Zheng, Zuowu, Fan Yang +9 · 8 citations
Social Sciences · #Bidding #Bridge (graph theory) #Digital Games and Media #FOS: Computer and information sciences #Generative grammar #Incentive compatibility #Information Retrieval (cs.IR) #Key (lock) #Online advertising #Payment
paper · pdf · doi:10.48550/arxiv.2505.17549
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2025/05/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Traditional online industrial advertising systems suffer from the limitations of multi-stage cascaded architectures, which often discard high-potential candidates prematurely and distribute decision logic across disconnected modules. While recent generative recommendation approaches provide end-to-end solutions, they fail to address critical advertising requirements of key components for real-world deployment, such as explicit bidding, creative selection, ad allocation, and payment computation. To bridge this gap, we introduce End-to-End Generative Advertising (EGA-V2), the first unified framework that holistically models user interests, point-of-interest (POI) and creative generation, ad allocation, and payment optimization within a single generative model. Our approach employs hierarchical tokenization and multi-token prediction to jointly generate POI recommendations and ad creatives, while a permutation-aware reward model and token-level bidding strategy ensure alignment with both user experiences and advertiser objectives. Additionally, we decouple allocation from payment using a differentiable ex-post regret minimization mechanism, guaranteeing approximate incentive compatibility at the POI level. Through extensive offline evaluations we demonstrate that EGA-V2 significantly outperforms traditional cascaded systems in both performance and practicality. Our results highlight its potential as a pioneering fully generative advertising solution, paving the way for next-generation industrial ad systems.