公开(公告)号：US09607314B1

公开(公告)日：2017-03-28

申请号：US15066225

申请日：2016-03-10

Applicant: Google Inc.

Inventor： Gaofeng Zhao ,  Yingwei Cui ,  Hui Tan ,  Bahman Rabii ,  Wei Chai

IPC: G06F17/00 ,  G06Q30/02

CPC classification number: G06Q30/0249 ,  G06F17/2247 ,  G06F17/30867 ,  G06Q30/0246 ,  G06Q30/0253 ,  G06Q30/0256 ,  G06Q30/0277

Abstract: Systems and methods of evaluating information in a computer network environment are provided. A data processing system can obtain or receive a content placement criterion, such as a keyword, associated with a content item and can determine a quality metric of the content placement criterion. The data processing system can identify a candidate content placement criterion and expand placement criteria associated with the content item to include the content placement criterion and the candidate content placement criterion based at least in part on an evaluation of the quality metric of the content placement criterion. The data processing system can expand placement criteria based in part on a throttling parameter. The data processing system can identify a correlation between a document and the placement criteria to identify appropriate content items for the document.

公开(公告)号：US20170061528A1

公开(公告)日：2017-03-02

申请号：US14836537

申请日：2015-08-26

Applicant: Google Inc.

Inventor： Shilpa Arora ,  Colin McCulloch ,  Niyati Yagnik ,  Creighton Thomas ,  Manohar Prabhu ,  Timothy Lipus ,  Michael Eugene Aiello ,  Yi Zhang ,  Ajay Kumar Bangla ,  Bahman Rabii ,  Gaofeng Zhao ,  Yingweii Cui

IPC: G06Q30/08 ,  G06N99/00 ,  G06F17/30

CPC classification number: G06Q30/08 ,  G06F17/3053 ,  G06F17/30864 ,  G06F17/3089 ,  G06N99/005

Abstract: The present disclosure selects third party content based on feedback. A selector identifies several content items including first and second content items (or more) responsive to a request. A machine learning engine determines a first feature of the first content item, a second feature of the second content item, and a third feature of the web page or a device associated with the request. The machine learning engine determines, responsive to the first feature and the third feature, a first score for the first content item based on a machine learning model generated using historical signals received from devices via a metadata channel formed from an electronic feedback interface. The machine learning engine determines a second score for the second content item responsive to the second feature and the third feature. A bidding module determines a price for the first content item based on the first and second scores.

Abstract translation: 本公开内容基于反馈来选择第三方内容。 选择器识别包括响应于请求的第一和第二内容项（或更多）的多个内容项。 机器学习引擎确定第一内容项目的第一特征，第二内容项目的第二特征以及网页的第三特征或与请求相关联的设备。 机器学习引擎响应于第一特征和第三特征，基于通过从电子反馈接口形成​​的元数据信道从设备接收的历史信号生成的机器学习模型来确定第一内容项的第一分数。 机器学习引擎响应于第二特征和第三特征确定第二内容项目的第二分数。 投标模块基于第一和第二分数来确定第一内容项目的价格。

公开(公告)号：US10223742B2

公开(公告)日：2019-03-05

申请号：US14836537

申请日：2015-08-26

Applicant: Google Inc.

Inventor： Shilpa Arora ,  Colin McCulloch ,  Niyati Yagnik ,  Creighton Thomas ,  Manohar Prabhu ,  Timothy Lipus ,  Michael Eugene Aiello ,  Yi Zhang ,  Ajay Kumar Bangla ,  Bahman Rabii ,  Gaofeng Zhao ,  Yingweii Cui

IPC: G06Q30/00 ,  G06F17/30 ,  G06Q30/08 ,  G06N99/00

Abstract: The present disclosure selects third party content based on feedback. A selector identifies several content items including first and second content items (or more) responsive to a request. A machine learning engine determines a first feature of the first content item, a second feature of the second content item, and a third feature of the web page or a device associated with the request. The machine learning engine determines, responsive to the first feature and the third feature, a first score for the first content item based on a machine learning model generated using historical signals received from devices via a metadata channel formed from an electronic feedback interface. The machine learning engine determines a second score for the second content item responsive to the second feature and the third feature. A bidding module determines a price for the first content item based on the first and second scores.

公开(公告)号：US09779411B1

公开(公告)日：2017-10-03

申请号：US15218903

申请日：2016-07-25

Applicant: Google Inc.

Inventor： Gaofeng Zhao ,  Ping Fu

IPC: G06F17/30 ,  G06Q30/02

CPC classification number: G06Q30/0242 ,  G06F17/3053 ,  G06Q30/0247

Abstract: Methods, systems, and apparatuses, including computer programs encoded on computer-readable media, for advertisement keyword scoring. A processing circuit receives a request for an advertisement to be provided to a user during a user session. The advertisement is to be provided alongside other content that is associated with a first plurality of keywords. A processing circuit identifies a plurality of advertisements based on the first plurality of keywords. Each of the plurality of advertisements are associated with a second plurality of keywords. The processing circuit calculates a keyword score for each of the second plurality of keywords for each of the plurality of advertisements. Based on the keyword score, one of the keywords for each of the plurality of the plurality of advertisements is selected. Based on a comparison of the selected keywords, the advertisement to be provided to the user is selected.

公开(公告)号：US09501549B1

公开(公告)日：2016-11-22

申请号：US14263554

申请日：2014-04-28

Applicant: Google Inc.

Inventor： Xuerui Wang ,  Gaofeng Zhao

IPC: G06F17/30

CPC classification number: G06F17/30587 ,  G06F17/30864

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for scoring criteria for content items. In one aspect, a method includes identifying a primary ranking signal and a set of auxiliary ranking signals for ranking a set of criteria for a content item. A primary score and a set of auxiliary scores can be identified for each particular criterion in the set of criteria. Each auxiliary score can be adjusted to generate adjusted auxiliary scores. The adjusting can include applying, to at least a portion of the auxiliary scores, a transformation function that reduces an amount of skewness among the auxiliary scores. A ranking score can be determined for each particular criterion based on a function of the primary score for the particular criterion and the adjusted auxiliary scores.

Abstract translation: 方法，系统和装置，包括在计算机存储介质上编码的计算机程序，用于评分内容项的标准。 一方面，一种方法包括识别主要排名信号和一组辅助排名信号，用于对内容项目的一组标准进行排名。 可以在该组标准中为每个特定标准确定主要分数和一组辅助分数。 可以调整每个辅助分数，以产生调整后的辅助分数。 调整可以包括向辅助分数的至少一部分应用减少辅助分数之间的偏度量的变换函数。 可以基于特定标准的初级得分的功能和调整后的辅助分数来确定每个特定标准的排名得分。