公开(公告)号：US20150356595A1

公开(公告)日：2015-12-10

申请号：US14297054

申请日：2014-06-05

Applicant: Yahoo! Inc.

Inventor： Ram Sriharsha ,  Supreeth Hosur Nagesh Rao

IPC: G06Q30/02

CPC classification number: G06Q30/0243 ,  G06Q30/0275

Abstract: Described herein are solutions for determining quality of online ads and matching the ads to content so that the content is not devalued by the ads. Such solutions may also identify relationships between ads and their influence on user engagement with host content. The solutions may also define and provide the relationships to advertisers, in forms of historical scores and projected scores. The historical scores may include historical elasticity scores and the projected scores may include projected elasticity scores. The scores may be determined per ad and content pair. The solutions can use the scores to influence ad pricing.

Abstract translation: 这里描述的是用于确定在线广告的质量并将广告与内容匹配以使内容不被广告贬值的解决方案。 这样的解决方案还可以识别广告之间的关系及其对用户与主机内容的参与的影响。 解决方案还可以以历史得分和预测分数的形式定义和提供与广告商的关系。 历史得分可以包括历史弹性得分，并且预测得分可以包括预测的弹性得分。 可以根据广告和内容对确定分数。 解决方案可以使用分数来影响广告定价。

公开(公告)号：US20160019578A1

公开(公告)日：2016-01-21

申请号：US14334494

申请日：2014-07-17

Applicant: Yahoo! Inc.

Inventor： Ram Sriharsha

IPC: G06Q30/02 ,  G06F17/30

CPC classification number: G06Q30/0242

Abstract: Example methods, apparatuses, and/or articles of manufacture are disclosed that may be implemented, in whole or in part, using one or more computing devices to provide analysis of the distribution of overlaps of logs of values.

Abstract translation: 公开了可以全部或部分地使用一个或多个计算设备实现对值日志重叠的分布的分析来实现的示例性方法，设备和/或制品。

公开(公告)号：US20150066646A1

公开(公告)日：2015-03-05

申请号：US14470704

申请日：2014-08-27

Applicant: Yahoo! Inc.

Inventor： Ram Sriharsha ,  Tim Tully ,  Supreeth Rao ,  Reynold Xin

IPC: G06Q30/02

CPC classification number: G06Q30/0256

Abstract: An advertising and data analysis platform may need to mine through vast amounts of data to come up with insights into advertising effectiveness, and measure and improve the effectiveness of advertising reach. Distributed network data analytics may be applied to ad matching/targeting, such that an in-memory cluster computing environment may be used with advertising data. For example, HADOOP may be utilized for distributed processing of the vast amounts of data and the HADOOP distributed file system (HDFS) is used for organizing communications and storage of that data. Satellite clusters or nodes may be generated that also utilize HDFS. For example, a SPARK or SHARK satellite cluster may be arranged to further utilize the HDFS of the HADOOP clusters.

Abstract translation: 广告和数据分析平台可能需要通过大量数据挖掘广告效果，并衡量和提高广告覆盖的有效性。 分布式网络数据分析可以应用于广告匹配/定位，使得内存中集群计算环境可以与广告数据一起使用。 例如，HADOOP可用于大量数据的分布式处理，并且HADOOP分布式文件系统（HDFS）用于组织该数据的通信和存储。 可以生成也利用HDFS的卫星群集或节点。 例如，可以安排SPARK或SHARK卫星簇来进一步利用HADOOP簇的HDFS。