用少样本快速适配不同广告商的预算分配，提升竞价收益。
An Adaptable Budget Planner for Enhancing Budget-Constrained Auto-Bidding in Online Advertising
- 分阶段预算分配框架，动态调整每阶段预算
- 少样本下显著提升竞价累积价值，效果稳定
- 适合需快速适配新广告商的实时竞价系统
在线广告中，广告主常使用自动竞价服务竞拍展示机会。典型目标是在预算约束下最大化赢得广告位的累计价值。然而，由于广告环境复杂且广告主多样，该问题极具挑战性。为此，我们提出ABPlanner——一种少样本可适配的预算规划器，用于改进预算约束下的自动竞价。ABPlanner基于分层竞价框架，将竞价过程分解为多个短周期阶段，并在各阶段间分配预算，使底层自动竞价器依据预算计划出价。其适应性通过类上下文强化学习的序列决策机制实现：针对每个广告主，ABPlanner逐轮调整预算分配策略，利用历史轮次数据作为当前决策的提示。这使得它仅需少量数据即可快速适应不同广告主，具备高样本效率。大量仿真实验与真实世界A/B测试验证了其有效性，证明其能显著提升自动竞价器的累计收益。
原文摘要 · Abstract (English)
In online advertising, advertisers commonly utilize auto-bidding services to bid for impression opportunities. A typical objective of the auto-bidder is to optimize the advertiser's cumulative value of winning impressions within specified budget constraints. However, such a problem is challenging due to the complex bidding environment faced by diverse advertisers. To address this challenge, we introduce ABPlanner, a few-shot adaptable budget planner designed to improve budget-constrained auto-bidding. ABPlanner is based on a hierarchical bidding framework that decomposes the bidding process into shorter, manageable stages. Within this framework, ABPlanner allocates the budget across all stages, allowing a low-level auto-bidder to bids based on the budget allocation plan. The adaptability of ABPlanner is achieved through a sequential decision-making approach, inspired by in-context reinforcement learning. For each advertiser, ABPlanner adjusts the budget allocation plan episode by episode, using data from previous episodes as prompt for current decisions. This enables ABPlanner to quickly adapt to different advertisers with few-shot data, providing a sample-efficient solution. Extensive simulation experiments and real-world A/B testing validate the effectiveness of ABPlanner, demonstrating its capability to enhance the cumulative value achieved by auto-bidders.
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。