Model for serving exploration traffic

Patent metadata

Field	Value
Publication number	US-2021390577-A1
Application number	US-202016897609-A
Country	US
Kind code	A1
Filing date	Jun 10, 2020
Priority date	Jun 10, 2020
Publication date	Dec 16, 2021
Grant date	—

Abstract

One or more computing devices, systems, and/or methods for implementing a model for serving exploration traffic are provided. An amount of spend by a content provider to provide content items of the content provider through a content serving platform to client devices of users is determined. A number of exploration impressions of users viewing exploration content items of the content provider over a timespan is determined. A return on exploration impression metric is determined for the content provider based upon a ratio of the amount of spend to the number of exploration impressions. The return on exploration metric is used to rank available exploration content items of content providers for serving exploration traffic.

First claim

What is claimed is:

1 . A method, comprising:
executing, on a processor of a computing device, instructions that cause the computing device to perform operations, the operations comprising:
determining an amount of spend over a timespan by a content provider to provide content items of the content provider through a content serving platform to client devices of users, wherein the content items of the content provider are available for serving non-exploration traffic from client devices in a deterministic manner using a user engagement model;
determining a number of exploration impressions of users viewing exploration content items of the content provider over the timespan, wherein the exploration content items of the content provider are available for serving exploration traffic from the client devices in a non-deterministic manner;
determining a return on exploration impression (ROEI) metric for the content provider based upon a ratio of the amount of spend to the number of exploration impressions; and
utilizing the ROEI metric to rank available exploration content items of content providers for serving the exploration traffic.

2 . The method of claim 1 , comprising:
serving the exploration traffic using exploration content items selected based upon percentages of exploration traffic assigned to content providers with exploration content items based upon ROEI metrics for the content providers.

3 . The method of claim 1 , comprising:
determining and enforcing a maximum number of simultaneous content items of the content provider that can be maintained for serving the exploration traffic based upon the ROEI and a configurable base value.

4 . The method of claim 1 , comprising:
determining the percentage of exploration traffic for the content provider based upon a minimum exploration percentage metric.

5 . The method of claim 1 , comprising:
generating an exploration model for the content provider platform to use for selecting exploration content items to serve for the exploration traffic.

6 . The method of claim 5 , wherein the generating an exploration model comprises:
determining ROEI metrics for the content providers; and utilizing the ROEI metrics to populate the exploration model with percentages of exploration traffic to serve using exploration content items of the content providers.

7 . The method of claim 5 , comprising:
populating the exploration model with a first entry for a first content provider, wherein the first entry comprises a first identifier of the first content provider, a first ROEI for the first content provider, and a first maximum number of simultaneous exploration content items for the first content provider.

8 . The method of claim 7 , comprising:
populating the exploration model with a second entry for a second content provider, wherein the second entry comprises a second identifier of the second content provider, a second ROEI for the second content provider, and a second maximum number of simultaneous exploration content items for the second content provider.

9 . The method of claim 1 , comprising:
tracking user engagement with an exploration content item for training the user engagement model to predict likelihoods of users engaging with the exploration content item.

10 . The method of claim 9 , comprising:
removing an exploration content item from an exploration bucket and adding the exploration content item into a non-exploration bucket as a content item.

11 . The method of claim 1 , wherein the determining an amount of spend comprises:
determining the amount of spend based upon an amount of non-exploration spend over the timespan by the content provider.

12 . The method of claim 1 , comprising:
populating the exploration model with an entry for an exploration content item.

13 . A computing device comprising:
a processor; and memory comprising processor-executable instructions that when executed by the processor cause performance of operations, the operations comprising:
determining an amount of spend over a timespan by a content provider to provide content items of the content provider through a content serving platform to client devices of users, wherein the content items of the content provider are used to serve non-exploration traffic;
determining a number of exploration impressions of users viewing exploration content items of the content provider over the timespan, wherein the exploration content items of the content provider are used to serve exploration traffic;
determining a return on exploration impression (ROEI) metric for the content provider based upon a ratio of the amount of spend to the number of exploration impressions; and
utilizing the ROEI metric to rank available exploration content items of content providers for serving the exploration traffic.

14 . The computing device of claim 13 , comprising:
generating an exploration model for the content provider platform to use for selecting exploration content items to serve for exploration traffic; and periodically updating the exploration model.

15 . The computing device of claim 13 , comprising:
generating an exploration model for the content provider platform to use for selecting exploration content items to serve for exploration traffic.

16 . The computing device of claim 15 , comprising:
populating the exploration model with an entry specifying a first percentage of exploration traffic for a first exploration content item.

17 . The computing device of claim 13 , comprising:
adjusting the percentage of exploration traffic based upon a minimum exploration percentage metric.

18 . A non-transitory machine readable medium having stored thereon processor-executable instructions that when executed cause performance of operations, the operations comprising:
determining an amount of spend over a timespan by a content provider to provide content items of the content provider through a content serving platform to client devices of users, wherein the content items of the content provider are available for serving non-exploration traffic; determining a number of exploration impressions of users viewing exploration content items of the content provider over the timespan, wherein the exploration content items of the content provider are available for serving exploration traffic; determining a return on exploration impression (ROEI) metric corresponding to a ratio of the amount of spend to the number of exploration impressions; and utilizing the ROEI metric to rank available exploration content items of content providers for serving the exploration traffic.

19 . The non-transitory machine readable medium of claim 18 , wherein the operations comprise:
determining and enforcing a maximum number of simultaneous content items of the content provider that can be maintained based upon the ROEI and a configurable base value.

20 . The non-transitory machine readable medium of claim 19 , wherein the operations comprise:
determining the configurable base value based upon a minimum percentage metric.

Assignees

• Oath Inc

Inventors

• Cohen Tal
• Koren Yair
• Shahar Abraham
• Zlotnik Alexander
• Kaplan Yohay

Classifications

• G06N20/00

  Machine learning · CPC title

• G06Q30/0275

  Auctions · CPC title

• G06Q30/0246 Primary

  Traffic · CPC title

• G06Q30/0244 Primary

  Optimization · CPC title

• G06Q10/06375

  Prediction of business process outcome or impact based on a proposed change · CPC title