公开(公告)号：US10445388B2

公开(公告)日：2019-10-15

申请号：US15708592

申请日：2017-09-19

Applicant: Google Inc.

Inventor： David Chan ,  Yueqing Wang ,  Aiyou Chen ,  James Koehler ,  Yuxue Jin ,  Michael Perry ,  Yunting Sun

IPC: G06F17/30 ,  G06F16/9535 ,  G06F16/29 ,  G06F16/955 ,  G06Q30/02

Abstract: Systems, methods, and computer-readable storage media that may be used to generate causal models and calculate a selection bias in mixed media. In some embodiments, the selection bias calculation is in search sponsored content in the context of mixed media modeling. In some embodiments, a method for search bias correction is based on the back-door criterion from causal inference.

公开(公告)号：US20160162975A1

公开(公告)日：2016-06-09

申请号：US14564123

申请日：2014-12-09

Applicant: Google Inc.

Inventor： Aiyou Chen ,  Jeffrey David Oldham

IPC: G06Q30/06 ,  G06N7/00

CPC classification number: G06Q30/0631 ,  G06N7/005

Abstract: Systems and methods of directed item consumption recommendations are disclosed which include generating, with a server, empirical transition matrix data that includes row data for a first item and column data for a second item, and an entry in the empirical transition matrix data for a number of users that acquire the second item after the first item, generating, with the server, metadata transition matrix data by partitioning items for each item metadata type, setting a uniform transition probability for all items in the partition, and summing the uniform transition probabilities across all metadata types, generating, with the server, transition probability matrix data by multiplying the metadata transition matrix data with a weight parameter, adding the result to the empirical transition matrix data, and normalizing each row, and providing item recommendations to a user computing device communicatively coupled to the server according to the generated transition probability matrix data.

Abstract translation: 公开了指导性项目消费推荐的系统和方法，其包括与服务器一起生成包括用于第一项目的行数据和第二项目的列数据的经验转换矩阵数据，以及用于数字的经验转移矩阵数据中的条目 在第一个项目之后获取第二个项目的用户，通过分配每个项目元数据类型的项目，通过服务器生成元数据转换矩阵数据，为分区中的所有项目设置均匀的转移概率，并将均匀转移概率相加 所有元数据类型，通过将元数据转换矩阵数据与权重参数相乘来产生与服务器的转移概率矩阵数据，将结果添加到经验转移矩阵数据中，并对每一行进行归一化，并向用户计算设备提供项目建议 根据所生成的转移概率矩阵数据通信地耦合到服务器 一个。

公开(公告)号：US20190087497A1

公开(公告)日：2019-03-21

申请号：US15708592

申请日：2017-09-19

Applicant: Google Inc.

Inventor： David Chan ,  Yueqing Wang ,  Aiyou Chen ,  James Koehler ,  Yuxue Jin ,  Michael Perry ,  Yunting Sun

IPC: G06F17/30

CPC classification number: G06F16/9535 ,  G06F16/29 ,  G06F16/955 ,  G06Q30/0201 ,  G06Q30/0242 ,  G06Q30/0243

Abstract: Systems, methods, and computer-readable storage media that may be used to generate causal models and calculate a selection bias in mixed media. In some embodiments, the selection bias calculation is in search sponsored content in the context of mixed media modeling. In some embodiments, a method for search bias correction is based on the back-door criterion from causal inference.

公开(公告)号：US20150363502A1

公开(公告)日：2015-12-17

申请号：US14305607

申请日：2014-06-16

Applicant: Google Inc.

Inventor： Aiyou Chen ,  James Robert Koehler ,  Nicolas Remy ,  Makoto Uchida

IPC: G06F17/30

CPC classification number: G06F16/9535

Abstract: Systems and techniques are provided for optimizing personalized recommendations with longitudinal data and a future objective. An identifier may be received for content items. A user content item history including a list identifying a previously acquired content item may be received. Content item metadata may be received including a correlation between the previously acquired content item and a content item for which an identifier was received, and a correlation between a content item for which an identifier was received and fulfillment of a future objective. A joint probability may be determined for each content item based on the user content item history and the content item metadata, including the probability that the content item will be acquired by the user after being recommended to the user and that a future objective will be fulfilled after the content item is acquired by the user.

Abstract translation: 提供系统和技术，用于通过纵向数据和未来目标优化个性化建议。 可以为内容项目接收标识符。 可以接收包括标识先前获取的内容项的列表的用户内容项历史。 可以接收包括先前获取的内容项目和接收到标识符的内容项目之间的相关性的内容项目元数据，以及接收到标识符的内容项目与未来目标的实现之间的相关性。 可以基于用户内容项目历史和内容项目元数据确定每个内容项目的联合概率，包括用户在被推荐给用户之后将获取内容项目并且将来将满足目标的概率 在用户获取内容项目之后。