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.