论文标题
请勿浪费金钱在广告支出上：通过凹面更改出价推荐
Do not Waste Money on Advertising Spend: Bid Recommendation via Concavity Changes
论文作者
论文摘要
在计算广告中，一个具有挑战性的问题是如何向广告商推荐给定预算限制的最佳投资回报（ROI）。本文提出了出价建议方案，发现点击预测曲线的凹度变化。推荐的投标是根据从显着增加（即向下凹的）到缓慢增加（向上凸出）的转折点得出的。基于参数学习的方法是通过求解相应的约束优化问题来应用的。关于现实世界广告方案的实证研究清楚地证明了业务指标的绩效增长（包括收入增加，点击增加和广告商的投资回报率增加）。
In computational advertising, a challenging problem is how to recommend the bid for advertisers to achieve the best return on investment (ROI) given budget constraint. This paper presents a bid recommendation scenario that discovers the concavity changes in click prediction curves. The recommended bid is derived based on the turning point from significant increase (i.e. concave downward) to slow increase (convex upward). Parametric learning based method is applied by solving the corresponding constraint optimization problem. Empirical studies on real-world advertising scenarios clearly demonstrate the performance gains for business metrics (including revenue increase, click increase and advertiser ROI increase).
