Real-Time Bidding (RTB) display advertising is a method for purchasing display advertising inventory in auctions that occur within milliseconds. The performance of RTB campaigns is generally measured with a series of Key Performance Indicators (KPIs) - measurements used to ensure that the campaign is cost-effective and that it is purchasing valuable inventory. While an RTB campaign should ideally meet all KPIs, simultaneous improvement tends to be very challenging, as an improvement to any one KPI risks a detrimental effect toward the others. Here we present an approach to simultaneously controlling multiple KPIs with a PID-based feedback-control system. This method generates a control score for each KPI, based on both the output of a PID controller module and a metric that quantifies the importance of each KPI for internal business needs. On regular intervals, this algorithm - Sequential Control - will choose the KPI with the greatest overall need for improvement. In this way, our algorithm is able to continually seek the greatest marginal improvements to its current state. Multiple methods of control can be associated with each KPI, and can be triggered either simultaneously or chosen stochastically, in order to avoid local optima. In both offline ad bidding simulations and testing on live traffic, our methods proved to be effective in simultaneously controlling multiple KPIs, and bringing them toward their respective goals.

中文翻译：

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展示广告中多目标的多变量反馈控制动态投标策略

实时竞价 (RTB) 展示广告是一种在几毫秒内发生的拍卖中购买展示广告库存的方法。RTB 活动的绩效通常通过一系列关键绩效指标 (KPI) 来衡量 - 用于确保活动具有成本效益并购买有价值的库存的衡量标准。虽然理想情况下 RTB 活动应该满足所有 KPI，但同时改进往往非常具有挑战性，因为对任何一个 KPI 的改进都有可能对其他 KPI 产生不利影响。在这里，我们提出了一种使用基于 PID 的反馈控制系统同时控制多个 KPI 的方法。该方法基于 PID 控制器模块的输出和量化每个 KPI 对内部业务需求的重要性的指标，为每个 KPI 生成一个控制分数。每隔一定时间，此算法（顺序控制）将选择总体最需要改进的 KPI。通过这种方式，我们的算法能够不断寻求对其当前状态的最大边际改进。多种控制方法可以与每个 KPI 相关联，并且可以同时触发或随机选择，以避免局部最优。在离线广告竞价模拟和实时流量测试中，我们的方法被证明可以有效地同时控制多个 KPI，并使它们实现各自的目标。多种控制方法可以与每个 KPI 相关联，并且可以同时触发或随机选择，以避免局部最优。在离线广告竞价模拟和实时流量测试中，我们的方法被证明可以有效地同时控制多个 KPI，并使它们实现各自的目标。多种控制方法可以与每个 KPI 相关联，并且可以同时触发或随机选择，以避免局部最优。在离线广告竞价模拟和实时流量测试中，我们的方法被证明可以有效地同时控制多个 KPI，并使它们实现各自的目标。