Real-time content integration based on machine learned selections

Patent metadata

Field	Value
Publication number	US-11025705-B1
Application number	US-202016749961-A
Country	US
Kind code	B1
Filing date	Jan 22, 2020
Priority date	May 31, 2017
Publication date	Jun 1, 2021
Grant date	Jun 1, 2021

Abstract

A content integration system is configured to rapidly select online content for distribution in response to a user-generated request. The content integration system can analyze available online content items and data describing the user to generate one or more numerical likelihoods estimating how the user will interact with each of the given online content items. The highest scoring content can be selected and transmitted to the user without a noticeable delay.

First claim

What is claimed is:

1. A method comprising:
receiving, by a network platform, from a client device, a request for a content collection comprising a plurality of content items, the plurality of content items displayed in sequence and navigable by performing a first user input action on a displayed one of the plurality of content item to cause display a next content item of the plurality of content items, at least one of the content items being a selectable content item that can be selected by performing a second user input action while the selectable content item is being displayed on the client device;
in response to the request for the content collection, identifying a plurality of candidate selectable content items submitted to the network platform by a plurality of additional client devices;
automatically generating, using a machine learning classifier, a relevancy value for each candidate selectable content item from the plurality of candidate selectable content items, the relevancy value indicating a likelihood that a user of the client device will perform the second user input action on candidate selectable content items if the candidate selectable content item is included the content collection and displayed on the client device;
automatically selecting one of the plurality of candidate selectable content items that has a highest relevancy value for inclusion in the content collection; and
causing, on the client device, presentation of the content collection with the selected one of the plurality of content items that has the highest relevancy value generated by the machine learning classifier.

2. The method of claim 1 , further comprising:
receiving, from the client device, a request for additional content generated by the user of the client device performing the second user input action while the one of the plurality of content items that has the highest relevancy value is displayed on the client device.

3. The method of claim 2 , further comprising:
causing, on the client device, presentation of the additional content in response to the user performing the second user input action on the client device while the one of the plurality of content items that has the highest relevancy value is displayed on the client device.

4. The method of claim 2 , wherein the selectable content item is configured to request the additional content in response to the second user input action being performed while the selectable content item is displayed.

5. The method of claim 4 , wherein the additional content is prespecified by one of the plurality of additional client devices that submitted the selectable content item.

6. The method of claim 1 , wherein the request for the content collection is generated in an active network session of an application executing on the client device of the user.

7. The method of claim 6 , further comprising:
identifying historical user data that describes past user actions of past users using the application; and
training the machine learning classifier on the historical user data.

8. The method of claim 7 , wherein the past user actions include browse path data, subscription data, and user profile data of the user.

9. The method of claim 8 , wherein the browse path data describes a browse path displayed to a past given user as the user navigates using the application.

10. The method of claim 8 , wherein the subscription data describes whether a past given user has subscribed to content on the application.

11. The method of claim 8 , wherein the user profile data describes user preferences of the user of the application.

12. The method of claim 1 , wherein the first user input action is a tap gesture and the second user input action is a swipe gesture.

13. The method of claim 1 , wherein the machine learning classifier implements a random forest scheme.

14. A system comprising:
one or more processors of a machine; and
a memory storing instructions that, when executed by the one or more processors, cause the machine to perform operations comprising:
receiving, from a client device, a request for a content collection comprising a plurality of content items, the plurality of content items displayed in sequence and navigable by performing a first user input action on a displayed one of the plurality of content item to cause display a next content item of the plurality of content items, at least one of the content items being a selectable content item that can be selected by performing a second user input action while the selectable content item is being displayed on the client device;
in response to the request for the content collection, identifying a plurality of candidate selectable content items submitted to the system by a plurality of additional client devices;
automatically generating, using a machine learning classifier, a relevancy value for each candidate selectable content item from the plurality of candidate selectable content items, the relevancy value indicating a likelihood that a user of the client device will perform the second user input action on candidate selectable content items if the candidate selectable content item is included the content collection and displayed on the client device;
automatically selecting one of the plurality of candidate selectable content items that has a highest relevancy value for inclusion in the content collection; and
causing, on the client device, presentation of the content collection with the selected one of the plurality of content items that has the highest relevancy value generated by the machine learning classifier.

15. The system of claim 14 , the operations further comprising:
receiving, from the client device, a request for additional content generated by the user of the client device performing the second user input action while the one of the plurality of content items that has the highest relevancy value is displayed on the client device.

16. The system of claim 15 , the operations further comprising:
causing, on the client device, presentation of the additional content in response to the user performing the second user input action on the client device while the one of the plurality of content items that has the highest relevancy value is displayed on the client device.

17. The system of claim 15 , wherein the selectable content item is configured to request the additional content in response to the second user input action being performed while the selectable content item is displayed.

18. The system of claim 17 , wherein the additional content is prespecified by one of the plurality of additional client devices that submitted the selectable content item.

19. The system of claim 14 , wherein the request for the content collection is generated in an active network session of an application executing on the client device of the user.

20. A non-transitory machine-readable storage device embodying instructions that, when executed by a machine, cause the machine to perform operations comprising:
receiving, from a client device, a request for a content collection comprising a plurality of content items, the plurality of content items displayed in sequence and navigable by performing a first user input action on a displayed one of the plurality of content item to cause display a next content item of the plurality of content items, at least one of the content items being a selectable content item that can be selected by performing a second user input action while the selectable content item is being displayed on the client d

Assignees

• Snap Inc

Inventors

• Brewer Jason
• Farnham Rodrigo B
• Lue David B
• Stucky-Mack Nicholas J

Classifications

• G06N5/01

  Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound · CPC title

• G06N7/01

  Probabilistic graphical models, e.g. probabilistic networks · CPC title

• G06N20/20 Primary

  Ensemble learning · 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