In this paper, we analyze a natural learning algorithm for uniform pacing of advertising budgets, equipped to adapt to varying ad sale platform conditions. On the demand side, advertisers face a fundamental technical challenge in automating bidding in a way that spreads their allotted budget across a given campaign subject to hidden, and potentially dynamic, "spent amount" functions. This automation and calculation must be done in runtime, implying a necessary low computational cost for the high frequency auction rate. Advertisers are additionally expected to exhaust nearly all of their sub-interval (by the hour or minute) budgets to maintain budgeting quotas in the long run. Our study analyzes a simple learning algorithm that adapts to the latent spent amount function of the market and learns the optimal average bidding value for a period of auctions in a small fraction of the total campaign time, allowing for smooth budget pacing in real-time. We prove our algorithm is robust to changes in the auction mechanism, and exhibits a fast convergence to a stable average bidding strategy. The algorithm not only guarantees that budgets are nearly spent in their entirety, but also smoothly paces bidding to prevent early exit from the campaign and a loss of the opportunity to bid on potentially lucrative impressions later in the period.
翻译:在本文中,我们分析了统一广告预算速度的自然学习算法,该算法能够适应不同的广告销售平台条件。在需求方面,广告商面临一个根本性的技术挑战,即以在隐藏和潜在动态的“消耗量”功能下分散其在特定竞选中分配的预算的方式使招标自动化。这种自动化和计算必须在正常时间进行,这意味着高频拍卖率的计算成本一定较低。广告商还预期几乎会耗尽其所有次互动(小时或分钟)预算,以长期维持预算配额。我们的研究分析了一种简单的学习算法,这种算法能够适应市场潜在耗资的功能,并在总竞选时间的一小部分时间里学习最佳的平均投标价值,从而可以使预算在实时时间上平稳地进行。我们证明我们的算法对于拍卖机制的变化是强大的,并显示出与稳定的平均招标战略的快速趋同。这种算法不仅保证预算几乎全部花完,而且能够平稳地进行投标,以防止在以后提前退出竞选并失去机会。