提出非自回归广告拍卖框架，提升广告系统效率与效果
NGA: Non-autoregressive Generative Auction with Global Externalities for Advertising Systems
- 采用非自回归并行解码，避免序列生成延迟
- 联合建模广告间关系与周边内容影响，捕捉全局外部性
- 适合大规模实时广告系统，兼顾收益与用户体验
在线广告拍卖是互联网商业的核心，需在提升收益的同时保障激励相容性、用户体验和实时效率。现有学习型拍卖框架虽能捕捉广告间的局部依赖关系，但难以处理全局外部性，且受限于串行生成带来的效率问题。本文提出非自回归生成拍卖（NGA），一种面向工业级在线广告的端到端框架。NGA通过联合建模广告间关系及邻近自然内容的影响，显式捕捉全局外部性；同时采用约束式并行解码策略与统一的多塔评估器，实现列表级收益与支付的高效计算。大量离线实验与商业平台上的大规模在线A/B测试表明，NGA在效果与效率上均持续优于现有方法。
原文摘要 · Abstract (English)
Online advertising auctions are fundamental to internet commerce, demanding solutions that not only maximize revenue but also ensure incentive compatibility, high-quality user experience, and real-time efficiency. While recent learning-based auction frameworks have improved context modeling by capturing intra-list dependencies among ads, they remain limited in addressing global externalities and often suffer from inefficiencies caused by sequential processing. In this work, we introduce the Non-autoregressive Generative Auction with global externalities (NGA), a novel end-to-end framework designed for industrial online advertising. NGA explicitly models global externalities by jointly capturing the relationships among ads as well as the effects of adjacent organic content. To further enhance efficiency, NGA utilizes a non-autoregressive, constraint-based decoding strategy and a parallel multi-tower evaluator for unified list-wise reward and payment computation. Extensive offline experiments and large-scale online A/B testing on commercial advertising platforms demonstrate that NGA consistently outperforms existing methods in both effectiveness and efficiency.
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