公开(公告)号：US20160189207A1

公开(公告)日：2016-06-30

申请号：US14740937

申请日：2015-06-16

Applicant: Yahoo! Inc.

Inventor： Jian Xu ,  Xuhui Shao ,  Jianjie Ma ,  Quan Lu

IPC: G06Q30/02

CPC classification number: G06Q30/0249 ,  G06Q30/0264 ,  G06Q30/0269 ,  G06Q30/0275 ,  G06Q30/0277

Abstract: Described herein are example systems and operations for enhancing targeted delivery of online content using action rate lift and/or A/B testing. These examples provide solutions to problems in targeted delivery of online content, such as the problem of not being able to identify audience and/or situational targets mostly or only influenced by the content item or campaign of concern. For example, described herein are solutions that can estimate AR lift associated with a content item, and then distribute the content item or similar content items accordingly. An AR lift model can be used and such a model can use machine learning, A/B testing, and/or statistical analysis.

Abstract translation: 这里描述的是使用动作速率提升和/或A / B测试来增强在线内容的目标传送的示例系统和操作。 这些例子为有针对性地提供在线内容的问题提供了解决方案，例如无法识别受众和/或情境目标的问题，主要或仅受内容项目或关注活动的影响。 例如，这里描述的是可以估计与内容项目相关联的AR提升，然后相应地分发内容项目或类似内容项目的解决方案。 可以使用AR提升模型，这种模型可以使用机器学习，A / B测试和/或统计分析。

公开(公告)号：US20160110755A1

公开(公告)日：2016-04-21

申请号：US14518601

申请日：2014-10-20

Applicant: Yahoo! Inc.

Inventor： Yury Smirnov ,  Quan Lu ,  Kuang-chih Lee

IPC: G06Q30/02

CPC classification number: G06Q30/0244

Abstract: Described herein are techniques and systems for online ad campaign tuning using proportional-integral-derivative (PID) controllers, such as proportional (P) controllers and proportional-integral (PI) controllers. Also, described herein are techniques and systems for shortening a learning/tuning phase of a PID controller used for optimizing an online ad campaign.

Abstract translation: 这里描述了使用比例积分微分（PID）控制器（例如比例（P）控制器和比例积分（PI））控制器在线广告活动调整的技术和系统。 此外，这里描述的是用于缩短用于优化在线广告活动的PID控制器的学习/调整阶段的技术和系统。

公开(公告)号：US20170098236A1

公开(公告)日：2017-04-06

申请号：US14873757

申请日：2015-10-02

Applicant: Yahoo! Inc.

Inventor： Kuang-chih Lee ,  Quan Lu ,  Giovanni Gardelli ,  Donglin Niu

IPC: G06Q30/02 ,  G06F17/30 ,  G06Q10/06

CPC classification number: G06Q30/0244 ,  G06Q10/067 ,  G06Q30/0247 ,  G06Q30/0275

Abstract: Described herein are example systems and operations for enhancing response prediction and bidding decision making. A feature recommendation controller may include a factorization machine that generates a set of combinations of contextual and advertiser features yielding high expected response rates. A bidding controller may implement a multi-arm bandit system that uses Thompson sampling to select an optimal one of the feature combinations that corresponds to a highest expected response rate. The bidding controller may compare the corresponding highest expected response rate with a threshold response rate associated with a pacing rate to determine whether to place a bid for a received ad request.

公开(公告)号：US20160180373A1

公开(公告)日：2016-06-23

申请号：US14573979

申请日：2014-12-17

Applicant: Yahoo! Inc.

Inventor： Jian Xu ,  Kuang-chih Lee ,  Wentong Li ,  Hang Qi ,  Quan Lu

IPC: G06Q30/02

CPC classification number: G06Q30/0244 ,  G06Q30/0272

Abstract: Described herein are techniques and systems for online ad campaign pacing. The techniques described herein use budget allocation along with the estimations of bids and response rates. With use of budget allocation, the techniques can use budget pacing to enhance impressions and maximize desired responses, such as desired click-through rates. These techniques focus on enhancing pacing and performance of ad campaigns, such as enhancing performance across distinct and/or unified online ad marketplaces. These techniques are especially useful in the context of a demand-side platform (DSP). In some examples, the techniques assume that impression supply is much larger than advertiser demand for impressions of their ads, so such techniques focus on selecting high performing inventory of ad space. Yet, with such a focus, a smooth or consistent delivery of ads over time is used.

Abstract translation: 这里描述了在线广告系列投放安排的技术和系统。 本文描述的技术使用预算分配以及出价和响应率的估计。 通过使用预算分配，这些技术可以使用预算投放安排来增加展示次数并最大化期望的响应，例如所需的点击率。 这些技术专注于提高广告系列的投放安排和效果，例如提升不同的和/或统一的在线广告市场的效能。 这些技术在需求侧平台（DSP）的上下文中特别有用。 在一些示例中，这些技术假定，对于广告的展示次数，展示供应量远远大于广告客户的需求，因此此类技术将重点放在选择高性能的广告空间广告资源。 然而，随着时间的推移，广告的流畅或一致性得到了这样的关注。