用组合博弈动态分配广告预算，适应市场变化并提升效果。
Adaptive Budget Optimization for Multichannel Advertising Using Combinatorial Bandits
- 基于饱和均值和变化点检测的自适应探索策略。
- 在多场广告活动中实现更高收益与更低遗憾。
- 适合需要实时优化预算的数字营销场景。
有效的预算分配对优化数字广告活动表现至关重要。然而，由于缺乏公开数据集和能验证真实广告复杂性的综合仿真环境，实用预算分配算法的发展仍受限。尽管多臂老虎机（MAB）算法已被广泛研究，但在非平稳环境中其效果会下降，而快速适应市场动态是关键。本文通过三项贡献推进该领域：首先，构建一个模拟多渠道广告活动长期运行的仿真环境，融合真实日志数据；其次，提出一种增强的组合带宽预算分配策略，利用饱和均值函数和带有变化点检测的目标探索机制，动态适应市场变化，并通过领域知识筛选目标区域以提升分配效率；最后，通过理论分析与实证结果表明，该方法在多个真实广告活动中持续优于基线策略，实现了更高的回报与更低的遗憾。
原文摘要 · Abstract (English)
Effective budget allocation is crucial for optimizing the performance of digital advertising campaigns. However, the development of practical budget allocation algorithms remain limited, primarily due to the lack of public datasets and comprehensive simulation environments capable of verifying the intricacies of real-world advertising. While multi-armed bandit (MAB) algorithms have been extensively studied, their efficacy diminishes in non-stationary environments where quick adaptation to changing market dynamics is essential. In this paper, we advance the field of budget allocation in digital advertising by introducing three key contributions. First, we develop a simulation environment designed to mimic multichannel advertising campaigns over extended time horizons, incorporating logged real-world data. Second, we propose an enhanced combinatorial bandit budget allocation strategy that leverages a saturating mean function and a targeted exploration mechanism with change-point detection. This approach dynamically adapts to changing market conditions, improving allocation efficiency by filtering target regions based on domain knowledge. Finally, we present both theoretical analysis and empirical results, demonstrating that our method consistently outperforms baseline strategies, achieving higher rewards and lower regret across multiple real-world campaigns.
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