Online bidding serves as a fundamental information system in mobile ecosystems, facilitating real-time ad allocation across billions of devices while optimizing both platform performance and user experience through data-driven decision making. Improving ad allocation efficiency is a long-standing research problem, as it directly enhances the economic outcomes for all participants in advertising platforms. This paper investigates the design of optimal boost factors in online bidding while incorporating quality value (the impact of displayed ads on publishers' long-term benefits). To address the divergent interests on quality, we establish a three-party auction framework with a unified welfare metric of advertiser and publisher. Within this framework, we derive the theoretical efficiency lower bound for C-competitive boost in second-price single-slot auctions, then design a novel quality-involved Boosting (q-Boost) algorithm for computing the optimal boost factor. Experimental validation on Alibaba's public dataset (AuctionNet) demonstrates 2%-6% welfare improvements over conventional approaches, proving our method's effectiveness in real-world settings.

翻译：在线竞价作为移动生态系统中的基础信息系统，通过数据驱动的决策制定，在数十亿设备间实现实时广告分配，同时优化平台性能和用户体验。提升广告分配效率是一个长期存在的研究问题，因为它直接增强广告平台中所有参与者的经济效益。本文研究了在线竞价中最优提升因子的设计，同时纳入了质量价值（即展示广告对发布商长期收益的影响）。为解决各方在质量利益上的分歧，我们建立了一个三方拍卖框架，采用广告主与发布商的统一福利度量指标。在此框架内，我们推导了第二价格单广告位拍卖中C竞争性提升的理论效率下界，进而设计了一种新颖的融入质量因素的提升算法（q-Boost）用于计算最优提升因子。在阿里巴巴公开数据集（AuctionNet）上的实验验证表明，相较于传统方法，我们的方法实现了2%-6%的福利提升，证明了该方法在真实场景中的有效性。