Distributing digital components based on predicted attributes

Patent metadata

Field	Value
Publication number	US-12130875-B2
Application number	US-202218008604-A
Country	US
Kind code	B2
Filing date	Jun 2, 2022
Priority date	Jun 2, 2022
Publication date	Oct 29, 2024
Grant date	Oct 29, 2024

Abstract

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selecting and distributing digital components based on predicted user attributes of users are described. In one aspect, a method includes obtaining data indicating content categories of content of the content pages accessed by the user during the user visits. A determination is made for an aggregate measure of each content category based on a quantity of user visits to content pages of the electronic resource of the publisher that included content classified as belonging to the content category. User attribute prediction data indicating previously predicted user attributes of the user is obtained. User attributes are predicted for the current visit of the user to the electronic resource of the publisher that is further used to select digital components for display with the electronic resource on a client device during the current visit.

First claim

What is claimed is:

1. A computer-implemented method, comprising:
obtaining, for each of a plurality of user visits by a user to content pages of an electronic resource of a publisher, data indicating content categories of content of the content pages accessed by the user during the user visits;
determining, for each content category, an aggregate measure based on a quantity of user visits by the user to content pages of the electronic resource of the publisher that included content classified as belonging to the content category;
obtaining, for each of the plurality of user visits by the user to the content pages of the electronic resource of the publisher, user attribute prediction data indicating previously predicted user attributes of the user that were predicted based on activity of the user at the electronic resource of the publisher during the user visit;
predicting, for a current visit of the user to the electronic resource of the publisher, user attributes of the user based on the aggregate measure for each content category and the obtained user attribute prediction data; and
causing a digital component selected based on the predicted user attributes to be displayed with the electronic resource on a client device of the user during the current visit.

2. The computer-implemented method of claim 1 , wherein obtaining data indicating content categories of the content of the content pages comprises evaluating digital content of the electronic resource displayed on the client device of the user and assigning the content to the content categories based on the evaluation.

3. The computer-implemented method of claim 1 , wherein determining the aggregate measure for each content category comprises determining a weighted sum of the visits by the user to content pages of the electronic resource of the publisher that included content classified as belonging to the content category.

4. The computer-implemented method of claim 3 , wherein determining the weighted sum of the user visits for a given content category comprises weighting each user visit based on a duration of time between a time at which the user visit occurred and a current time.

5. The computer-implemented method of claim 1 , wherein determining the aggregate measure for each content category comprises:
assigning, for each user visit by the user to a content page of the electronic resource of the publisher, a visit value based on whether the content page included content classified as belonging to the content category; and
determining an average of the visit values for the content category.

6. The computer-implemented method of claim 1 , wherein causing the digital component selected based on the predicted user attributes to be displayed with the electronic resource on a client device of the user during the current visit comprises:
receiving a digital component request comprising the predicted user attributes and one or more contextual signals that indicate a context of one or more content pages of the electronic resources visited during the current user visit;
selecting the digital component based on the predicted attributes and the one or more contextual signals; and
sending the digital component to the client device of the user.

7. The computer-implemented method of claim 6 , wherein predicting, for the current visit of the user to the electronic resource of the publisher, the user attributes of the user based on the aggregate measure for each content category and the obtained user attribute prediction data comprises:
providing, as input to a context-based attribute prediction model trained to predict user attributes based on contextual signals, the aggregate measure for each content category, the user attribute prediction data for each user visit by the user to the content pages of the electronic resource of the publisher, and the one or more contextual signals; and
receiving, as an output of the context-based attribute prediction model, the predicted user attributes of the user.

8. A system comprising:
a memory device; and
one or more processors configured to interact with the memory device and configured to perform operations, including:
obtaining, for each of a plurality of user visits by a user to content pages of an electronic resource of a publisher, data indicating content categories of content of the content pages accessed by the user during the user visits;
determining, for each content category, an aggregate measure based on a quantity of user visits by the user to content pages of the electronic resource of the publisher that included content classified as belonging to the content category;
obtaining, for each of the plurality of user visits by the user to the content pages of the electronic resource of the publisher, user attribute prediction data indicating previously predicted user attributes of the user that were predicted based on activity of the user at the electronic resource of the publisher during the user visit;
predicting, for a current visit of the user to the electronic resource of the publisher, user attributes of the user based on the aggregate measure for each content category and the obtained user attribute prediction data; and
causing a digital component selected based on the predicted user attributes to be displayed with the electronic resource on a client device of the user during the current visit.

9. The system of claim 8 , wherein obtaining data indicating content categories of the content of the content pages comprises evaluating digital content of the electronic resource displayed on the client device of the user and assigning the content to the content categories based on the evaluation.

10. The system of claim 8 , wherein determining the aggregate measure for each content category comprises determining a weighted sum of the visits by the user to content pages of the electronic resource of the publisher that included content classified as belonging to the content category.

11. The system method of claim 10 , wherein determining the weighted sum of the user visits for a given content category comprises weighting each user visit based on a duration of time between a time at which the user visit occurred and a current time.

12. The system of claim 8 , wherein determining the aggregate measure for each content category comprises:
assigning, for each user visit by the user to a content page of the electronic resource of the publisher, a visit value based on whether the content page included content classified as belonging to the content category; and
determining an average of the visit values for the content category.

13. The system of claim 8 , wherein causing the digital component selected based on the predicted user attributes to be displayed with the electronic resource on a client device of the user during the current visit comprises:
receiving a digital component request comprising the predicted user attributes and one or more contextual signals that indicate a context of one or more content pages of the electronic resources visited during the current user visit;
selecting the digital component based on the predicted attributes and the one or more contextual signals; and
sending the digital component to the client device of the user.

14. The computer-implemented method of claim 13 , wherein predicting, for the current visit of the user to the electronic resource of the publisher, the user attributes of the user based on the aggregate measure for each content category and the obtained user attribute prediction data comprises:
providing, as input to a context-based attribute prediction model trained to predict us

Assignees

• Google Llc

Inventors

• Huang Wei
• Mauser Arne

Classifications

• H04L67/55

  Push-based network services · CPC title

• H04L67/306

  User profiles · CPC title

• H04L67/535

  Tracking the activity of the user (network monitoring arrangements H04L43/00; recording of computer activity G06F11/34) · CPC title

• G06N20/00

  Machine learning · CPC title

• G06F16/906

  Clustering; Classification · CPC title