赞助搜索市场的自适应广告加载设计：证据、理论与部署

中文摘要 AI 辅助

广告加载设计是赞助搜索中供应方的核心决策。更多赞助位虽能增加收入，但可能排挤自然搜索结果并降低用户体验。我们在安卓应用商店进行大规模随机现场实验，超五百万用户接触一至六个赞助位。增加广告加载量最多可使收入提高43%，但总搜索转化率最多降低5%，每日参与度最多降低2.2%。不同查询的效果存在异质性。基于此，我们设计并部署了新型自适应算法e-LAAL，它结合了LAAL和静态探索策略，还提供了有限时间动态遗憾保证。在面向2230万用户和7760万次搜索的平台级生产部署中，e-LAAL改善了收益与转化率的权衡，优于统一和历史查询相关的静态基准。

英文摘要

Ad-load design is a central supply-side decision in sponsored search: more sponsored slots can raise revenue, but may crowd out organic results and degrade user outcomes. We study this trade-off using a large-scale randomized field experiment on an Android app store, where over five million users are exposed to one through six sponsored slots. Increasing ad load raises revenue by up to 43%, but reduces total search conversions by up to 5% and daily engagement by up to 2.2%. These average effects mask substantial heterogeneity: additional slots generate large revenue gains for high-ad-conversion queries, but little or negative marginal revenue for low-conversion queries. The trade-off also shifts within query as advertiser composition changes, such as brand-advertiser presence. Motivated by these findings, we design and deploy a novel adaptive algorithm -- exploration-augmented Locally Adaptive Ad Load (e-LAAL). e-LAAL combines LAAL, a model-free query-level decision rule that updates ad-load recommendations using recent outcomes, with static exploration arms that maintain support and provide fixed-policy counterfactual benchmarks. We provide a finite-time dynamic-regret guarantee for the e-LAAL architecture. In a platform-level production deployment serving 22.3 million users and 77.6 million searches, e-LAAL improves the empirical revenue--conversion trade-off relative to deployed static benchmarks and outperforms uniform and historical query-dependent static benchmarks.

发表机构

• University of Washington（华盛顿大学）

机构由 AI 辅助整理，请以论文原文为准。

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54 pages