PlatformBid：来自统一广告平台视角的自动竞价基准

中文摘要 AI 辅助

实时竞价是计算广告的核心，包含三个要素：供应方平台（SSP）出售广告曝光量、需求方平台（DSP）代表广告主竞价，以及广告交易平台在两者间开展拍卖。传统自动竞价算法仅聚焦于DSP侧，通过调整竞价来最大化广告主的转化量。然而，当前大型广告平台（如社交媒体和电商公司）已在内部整合了SSP、DSP和广告交易平台的功能。从这类广告平台的视角来看，自动竞价算法的目标不仅是最大化广告主的转化量，还需最大化平台的总收益。鉴于缺乏以平台为中心的评估框架，且推进自动竞价研究的需求迫切，我们提出PlatformBid——首个从统一广告平台视角设计的综合基准。为准确反映现实中的自动竞价场景，我们定义了三种代表性设置：（1）广告主采用相同算法的同质竞争；（2）广告主采用不同算法策略的异质竞争；（3）部分广告主在黑色星期五等促销活动中增加预算以提升销量的促销竞争。我们在这些设置下系统评估了广泛的现有自动竞价方法，涵盖经典控制方法、基于强化学习（RL）的方法以及近期的生成式方法。除这些方法外，我们还提出了一种基于流匹配的新型自动竞价方法，名为BidFlow，该方法利用流匹配方法的高表达性策略表示，以有效处理动态竞争环境。在快手开展的在线实验进一步显示，目标成本提升了+0.68%，为PlatformBid的离线-在线一致性提供了部署证据。

英文摘要

Real-time bidding is central to computational advertising, comprising three elements: Supply Side Platform (SSP) selling ad impressions, Demand Side Platform (DSP) bidding for advertisers, and Ad Exchange conducting auctions between them. Traditional auto-bidding algorithms focus solely on the DSP side, maximizing advertiser conversions by adjusting bids against competitors. However, current big ad platforms, such as social media and e-commerce companies, now integrate SSP, DSP, and Ad Exchange functions internally. From such ad platforms' perspective, the goal of the auto-bidding algorithms is not only to maximize the advertisers' conversions, but also the total revenue of the platform. Given the lack of platform-centric evaluation frameworks and the pressing need to advance auto-bidding research, we propose PlatformBid - the first comprehensive benchmark designed from a unified ad platform's perspective. To accurately reflect the real-world auto-bidding scenarios, we define three representative settings: (1) homogeneous competition with identical algorithms across advertisers, (2) heterogeneous competition with diverse algorithmic strategies, and (3) promotional competition where some advertisers surge budgets for boosting sales during promotional events like Black Friday. We systematically evaluate a broad spectrum of existing auto-bidding methods across these settings, encompassing classical control methods, RL-based methods, and recent generative methods. Besides these methods, we further propose a novel auto-bidding method based on flow-matching, termed BidFlow, which leverages the flow-matching method's expressive policy representation to effectively handle dynamic competitive environments. Online experiments on Kuaishou further show a +0.68\% improvement in target cost, providing deployment evidence for the offline-online consistency of PlatformBid.

发表机构

• Southeast University（东南大学）
• Kuaishou Technology（快手科技）
• Nanyang Technological University（南洋理工大学）

机构由 AI 辅助整理，请以论文原文为准。