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：US20190065638A1

公开(公告)日：2019-02-28

申请号：US15693326

申请日：2017-08-31

Applicant: Google Inc.

Inventor： Yunting Sun ,  Yuxue Jin ,  James Koehler ,  Xiaojing Huang ,  David Chan ,  Yueqing Wang ,  Conor Sontag ,  Shi Zhong ,  Luis Gonzalez Perez

IPC: G06F17/50 ,  G06F17/18

Abstract: Systems, methods, and computer-readable storage media that may be used to generate a Bayesian hierarchical model. One method includes generating a plurality of geographic regions by grouping one or more geographic sub-regions into each of the plurality of geographic regions. The method further includes receiving data for the geographic sub-regions, the data including responses, content inputs, content types, and location identifiers. The method further includes generating geo-level data from the received data by grouping the responses and content inputs of the received data based on a correlation of the location identifiers of the received data to the plurality of geographic regions. The method includes fitting a Bayesian hierarchical model based on at least the geo-level data, the content types, and the geographic regions and determining a content input mix for the content types for each geographic region based on the Bayesian hierarchical model and a content input constraint.

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：US10706191B2

公开(公告)日：2020-07-07

申请号：US15693326

申请日：2017-08-31

Applicant: Google Inc.

Inventor： Yunting Sun ,  Yuxue Jin ,  James Koehler ,  Xiaojing Huang ,  David Chan ,  Yueqing Wang ,  Conor Sontag ,  Shi Zhong ,  Luis Gonzalez Perez

IPC: H04N21/25 ,  G06F30/20 ,  G06F17/18 ,  H04L29/06 ,  G06T7/11 ,  G06Q30/02 ,  G06F111/10

Abstract: Systems, methods, and computer-readable storage media that may be used to generate a Bayesian hierarchical model. One method includes generating a plurality of geographic regions by grouping one or more geographic sub-regions into each of the plurality of geographic regions. The method further includes receiving data for the geographic sub-regions, the data including responses, content inputs, content types, and location identifiers. The method further includes generating geo-level data from the received data by grouping the responses and content inputs of the received data based on a correlation of the location identifiers of the received data to the plurality of geographic regions. The method includes fitting a Bayesian hierarchical model based on at least the geo-level data, the content types, and the geographic regions and determining a content input mix for the content types for each geographic region based on the Bayesian hierarchical model and a content input constraint.

公开(公告)号：US20190080246A1

公开(公告)日：2019-03-14

申请号：US15704939

申请日：2017-09-14

Applicant: Google Inc.

Inventor： Yunting Sun ,  David Chan ,  James Koehler ,  Yuxue Jin ,  Yueqing Wang

IPC: G06N5/02

Abstract: Systems, methods, and computer-readable storage media that may be used to generate a category Bayesian hierarchical model. One method includes receiving a brand data set for each of a plurality of brands within a category, each brand data set comprising content input for a particular brand of the plurality of brands for a plurality of media channels at a plurality of times and a response for the particular brand of the plurality of brands at the plurality of times. The method includes determining a plurality of informative priors by generating a category Bayesian hierarchical model based on the plurality of brand data sets and a plurality of weak priors. The method further includes generating a brand Bayesian hierarchical model that models response for the particular brand for each of the plurality of media channels based on the brand data set for the particular brand and the plurality of informative priors.