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公开(公告)号：US11983089B2
公开(公告)日：2024-05-14
申请号：US17278395
申请日：2019-12-05
Applicant: Google LLC
Inventor： Xinlong Bao , Ali Nasiri Amini , Jing Wang , Mert Dikmen , Amy Richardson , Dinah Shender , Junji Takagi , Sen Li , Ruoyi Jiang , Yang Jiao , Yang Zhang , Zhuo Zhang
CPC classification number: G06F11/3433 , G06F11/3428 , G06N20/00
Abstract: Methods, systems, and computer programs encoded on a computer storage medium, for training and using machine learning models are disclosed. Methods include creating a model that represents relationships between user attributes, content exposures, and performance levels for a target action using organic exposure data specifying one or more organic exposures experienced by a particular user over a specified time prior to performance of a target action by the particular user and third party exposure data specifying third party exposures of a specified type of digital component to the particular user over the specified time period. Using the model, an incremental performance level attributable to each of the third party exposures at an action time when the target action was performed by the particular user is determined. Transmission criteria for at least some digital components to which the particular user was exposed are modified based on the incremental performance.
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公开(公告)号：US20210326233A1
公开(公告)日：2021-10-21
申请号：US17278395
申请日：2019-12-05
Applicant: Google LLC
Inventor： Xinlong Bao , Ali Nasiri Amini , Jing Wang , Mert Dikmen , Amy Richardson , Dinah Shender , Junji Takagi , Sen Li , Ruoyi Jiang , Yang Jiao , Yang Zhang , Zhuo Zhang
Abstract: Methods, systems, and computer programs encoded on a computer storage medium, for training and using machine learning models are disclosed. Methods include creating a model that represents relationships between user attributes, content exposures, and performance levels for a target action using organic exposure data specifying one or more organic exposures experienced by a particular user over a specified time prior to performance of a target action by the particular user and third party exposure data specifying third party exposures of a specified type of digital component to the particular user over the specified time period. Using the model, an incremental performance level attributable to each of the third party exposures at an action time when the target action was performed by the particular user is determined. Transmission criteria for at least some digital components to which the particular user was exposed are modified based on the incremental performance.
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公开(公告)号：US20240248825A1
公开(公告)日：2024-07-25
申请号：US18625830
申请日：2024-04-03
Applicant: Google LLC
Inventor： Xinlong Bao , Ali Nasiri Amini , Jing Wang , Mert Dikmen , Amy Richardson , Dinah Shender , Junji Takagi , Sen Li , Ruoyi Jiang , Yang Jiao , Yang Zhang , Zhuo Zhang
CPC classification number: G06F11/3433 , G06F11/3428 , G06N20/00
Abstract: Methods, systems, and computer programs encoded on a computer storage medium, for training and using machine learning models are disclosed. Methods include creating a model that represents relationships between user attributes, content exposures, and performance levels for a target action using organic exposure data specifying one or more organic exposures experienced by a particular user over a specified time prior to performance of a target action by the particular user and third party exposure data specifying third party exposures of a specified type of digital component to the particular user over the specified time period. Using the model, an incremental performance level attributable to each of the third party exposures at an action time when the target action was performed by the particular user is determined. Transmission criteria for at least some digital components to which the particular user was exposed are modified based on the incremental performance.
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公开(公告)号：US20250004797A1
公开(公告)日：2025-01-02
申请号：US18260645
申请日：2023-06-14
Applicant: Google LLC
Inventor： Lisa Michelle Jakobovits , Jonathan James Mulligan , Mert Dikmen , Xinlong Bao , John Chen , Jing Wang
Abstract: Example embodiments of the present disclosure provide for an example method. The example method includes obtaining session data descriptive of a plurality of user sessions, the plurality of user sessions respectively including an interaction with an input element rendered at a user device and a request for a resource associated with the input element. The example method includes obtaining, using a first machine-learned model, a plurality of weights associated with the plurality of user sessions by, for a respective user session of the plurality of user sessions: inputting, to the first machine-learned model, data descriptive of one or more characteristics of the respective user session; and obtaining, from the first machine-learned model, a respective weight of the plurality of weights, the respective weight indicative of an incremental probability of the request conditioned on rendering of the input element. The example method includes updating, based on the plurality of weights, a second machine-learned model to optimize candidate proposals for participation in a real-time content selection process for populating a user interface with one or more selected input elements.
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