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：US20160189201A1

公开(公告)日：2016-06-30

申请号：US14583457

申请日：2014-12-26

Applicant: Yahoo! Inc.

Inventor： Xuhui Shao

IPC: G06Q30/02

CPC classification number: G06Q30/0243 ,  G06Q30/0254 ,  G06Q30/0277

Abstract: Described herein are example systems and operations for enhancing targeted advertising using A/B testing. These examples provide solutions to problems in targeted advertising, such as the problem of not being able to identify audience and/or situational targets mostly or only influenced by the ad or ad campaign of concern. For example, described herein are solutions that can build a pair of differential behavioral data sets similar to an A/B clinical study. Then two or more models can be generated on each data set. In an example, these models can be based on machine learning and/or statistical analysis. The differential learning between the two or more models can then be used to enhance predictions of desired response probabilities mostly or only due to the influence of the ad or ad campaign being modeled.

Abstract translation: 这里描述的是使用A / B测试增强目标广告的示例系统和操作。 这些示例为有针对性的广告中的问题提供了解决方案，例如无法主要或仅受广告或广告活动影响的受众和/或情境目标的问题。 例如，这里描述的是可以构建类似于A / B临床研究的一对差分行为数据集的解决方案。 然后可以在每个数据集上生成两个或多个模型。 在一个例子中，这些模型可以基于机器学习和/或统计分析。 然后，可以使用两个或多个模型之间的差异学习来主要或仅由于正在建模的广告或广告活动的影响来增强对期望的响应概率的预测。