用强化学习解决广告冷启动问题，提升平台长期收益
Optimizing Online Advertising with Multi-Armed Bandits: Mitigating the Cold Start Problem under Auction Dynamics
- 基于多臂老虎机设计新算法，兼顾探索与收益
- 实验证明可显著提升新广告点击率预测准确率
- 适合广告系统优化与平台运营者参考
在线广告平台常面临冷启动问题：新广告因缺乏点击行为数据，难以准确预测点击率（CTR）。同时，旧广告的CTR也可能因早期表现不佳而被长期低估。该问题严重影响平台长期收益。为此，本文在多臂老虎机（MAB）框架下，针对基于位置的模型（PBM）设计了一种类似上置信界（UCB）的算法，专用于按点击付费（PPC）拍卖机制。算法兼具理论保障与实践可行性：理论上给出了预算后悔的上界估计，并在合成数据与真实数据上验证了其有效性。实验表明，该方法不仅能提升平台长期盈利能力，还通过控制探索与利用的平衡，保障短期利润。
原文摘要 · Abstract (English)
Online advertising platforms often face a common challenge: the cold start problem. Insufficient behavioral data (clicks) makes accurate click-through rate (CTR) forecasting of new ads challenging. CTR for "old" items can also be significantly underestimated due to their early performance influencing their long-term behavior on the platform. The cold start problem has far-reaching implications for businesses, including missed long-term revenue opportunities. To mitigate this issue, we developed a UCB-like algorithm under multi-armed bandit (MAB) setting for positional-based model (PBM), specifically tailored to auction pay-per-click systems. Our proposed algorithm successfully combines theory and practice: we obtain theoretical upper estimates of budget regret, and conduct a series of experiments on synthetic and real-world data that confirm the applicability of the method on the real platform. In addition to increasing the platform's long-term profitability, we also propose a mechanism for maintaining short-term profits through controlled exploration and exploitation of items.
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