论文标题
风险了解在线显示广告的出价优化
Risk-Aware Bid Optimization for Online Display Advertisement
论文作者
论文摘要
这项研究重点介绍了在线展示广告的实时出价设置中的出价优化问题，在线展示广告中，广告商或广告商的代理人可以访问网站访问者的功能和广告插槽的类型，以决定预定的总广告预算，以决定最佳的出价价格。我们提出了一种风险意识的数据驱动的BID优化模型，该模型通过利用历史数据来预先设计竞标政策，将广告机会的类型映射到投标价格，并考虑在给定时间内违反预算约束的风险，从而最大程度地提高了广告商的预期利润。在采用拉格朗日放松后，我们为最佳招标策略提供了参数化的闭合形式表达式。使用现实世界中的数据集，我们证明，与风险中立模型相比，我们的规避风险方法可以有效地控制预算过度筹集预算的风险，同时获得竞争性的利润水平，并且可以使用最先进的数据驱动的风险意外的竞标方法。
This research focuses on the bid optimization problem in the real-time bidding setting for online display advertisements, where an advertiser, or the advertiser's agent, has access to the features of the website visitor and the type of ad slots, to decide the optimal bid prices given a predetermined total advertisement budget. We propose a risk-aware data-driven bid optimization model that maximizes the expected profit for the advertiser by exploiting historical data to design upfront a bidding policy, mapping the type of advertisement opportunity to a bid price, and accounting for the risk of violating the budget constraint during a given period of time. After employing a Lagrangian relaxation, we derive a parametrized closed-form expression for the optimal bidding strategy. Using a real-world dataset, we demonstrate that our risk-averse method can effectively control the risk of overspending the budget while achieving a competitive level of profit compared with the risk-neutral model and a state-of-the-art data-driven risk-aware bidding approach.
