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Contribution incrementality machine learning models

A model and incremental execution technology, applied in the field of data processing and machine learning models, can solve problems such as difficult to assess the impact of follow-up activities

Pending Publication Date: 2021-12-14
GOOGLE LLC
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, it is difficult to assess the amount of influence each prior activity has on subsequent activities

Method used

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  • Contribution incrementality machine learning models
  • Contribution incrementality machine learning models
  • Contribution incrementality machine learning models

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0021] Users connected to the Internet are exposed to various digital content (eg, search results, web pages, digital components, news articles, social media posts, audio information output by digital assistant devices). Some of these exposures to content may contribute to the user's ability to perform a specified target action. For example, a user exposed to a webpage about an endangered species may sign up for a newsletter related to helping save the endangered species, where subscribing to the newsletter may be considered a designated targeted action. Similarly, a user who is exposed to information about a particular type of mobile device may eventually acquire that particular type of mobile device, where acquiring a mobile device may be considered a targeted action. Examples of targeted actions could also include registering / registering for a service in a website, adding an item to an online shopping cart, downloading a white paper, or acquiring a product.

[0022] It can...

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PUM

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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.

Description

technical field [0001] This specification deals with data processing and machine learning models. Background technique [0002] Users engage in various online activities, and each of these activities results in the user being exposed to different information. Subsequent online activities of users may be influenced by their previous activities and information exposed to them. However, it is difficult to assess the amount of influence each prior activity has on subsequent activities. Contents of the invention [0003] Overall, an innovative aspect of the subject matter described in this specification can be embodied in methods that include the following operations: creating models that represent relationships between user attributes, content exposure, and performance levels of specified target actions; obtaining organic Exposure data specifying one or more organic exposures experienced by a particular user within a specified time period before the specified user performed ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q30/02
CPCG06Q30/0201G06Q30/0202G06N20/00G06N5/02G06F11/3428G06F11/3433
Inventor X.鲍A.纳西里阿米尼J.王M.迪克门A.理查德森D.申德J.塔卡吉S.李R.蒋Y.焦Y.张Z.张
Owner GOOGLE LLC