广告竞价新范式：用边际收益替代点击收入，更精准控制预算
The (Marginal) Value of a Search Ad: An Online Causal Framework for Repeated Second-price Auctions
- 将广告价值定义为胜败对比的因果效应，而非单纯点击收益
- 在多次二价拍卖中实现最优学习速率，降低浪费性支出
- 利用二价支付规则隐含信息，优于一价拍卖的学习算法
数字广告中的自动出价算法通常将广告机会的价值等同于展示或点击带来的收入，导致资源浪费。实际上，真实价值应是获得付费曝光的边际收益：即使未赢得广告位，广告主仍可能通过自然搜索结果（如Google或Amazon）获益。受近期研究启发，本文将广告价值建模为处理效应——即竞拍胜败之间的结果差异，并从因果视角研究二价（Vickrey）拍卖中的在线学习出价问题。我们提出算法，在多种反馈模型下达到率最优后悔值。关键创新在于利用二价支付规则揭示的信息，显著优于一价拍卖中的类似学习问题。
原文摘要 · Abstract (English)
Existing auto-bidding algorithms in digital advertising often treat the value of an ad opportunity as the revenue obtained when an ad is shown and/or clicked, and bid accordingly. This can lead to wasteful spending because the true value is the marginal gain from paid exposure: even without winning a sponsored slot, an advertiser may still earn revenue via an organic search result (e.g., on Google or Amazon). Motivated by recent work, we model ad value as a treatment effect--the outcome difference between winning and losing the auction--and study online learning for bidding in second-price (Vickrey) auctions under this causal perspective. We develop algorithms that attain rate-optimal regret under several feedback models. A key ingredient exploits the information revealed by the second-price payment rule, which strictly improves regret relative to analogous learning problems in first-price auctions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。