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Optimizing Online Advertising with Multi-Armed Bandits: Mitigating the Cold Start Problem under Auction Dynamics

Anastasiia Soboleva, Andrey Pudovikov, Roman Snetkov, Alina Babenko, Egor Samosvat, Yuriy Dorn

arXiv

arXiv:2502.01867

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Optimizing Online Advertising with Multi-Armed Bandits: Mitigating the Cold Start Problem under Auction Dynamics

@misc{soboleva2025-advertising-bandits,
  title     = {Optimizing Online Advertising with Multi-Armed Bandits: Mitigating the Cold Start Problem under Auction Dynamics},
  author    = {Anastasiia Soboleva and Andrey Pudovikov and Roman Snetkov and Alina Babenko and Egor Samosvat and Yuriy Dorn},
  year      = {2025},
  howpublished = {arXiv},
  note      = {Preprint},
  eprint    = {2502.01867},
  archivePrefix = {arXiv},
  url       = {https://arxiv.org/abs/2502.01867}
}

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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.