Content evaluation based on users browsing history

Patent metadata

Field	Value
Publication number	US-10217132-B1
Application number	US-201514864380-A
Country	US
Kind code	B1
Filing date	Sep 24, 2015
Priority date	Jun 18, 2012
Publication date	Feb 26, 2019
Grant date	Feb 26, 2019

Abstract

A computerized method and apparatus for evaluating content on a computer network. The method includes obtaining a quality score of content configured for display with a web page, wherein the quality score is based at least in part on keywords associated with the content and either a search query or metadata associated with the web page. The method also includes identifying a user metric of a computing device associated with the search query or the metadata. The method further includes generating an adjusted quality score of the content based on the quality score and the user metric. The method also includes selecting a parameter for an auction based on the adjusted quality score, wherein the parameter indicates a relation between a bid value based auction and a content quality based auction.

First claim

What is claimed is:

1. A method of evaluating content on a computer network, comprising:
identifying, by a data processing system, a quality score of content configured for display with a resource;
identifying a user metric of a computing device based on activity of the user;
generating an adjusted quality score of the content based on the quality score and the user metric;
selecting a parameter for an auction based on the adjusted quality score, the parameter indicating a normalized ratio between a bid value-based auction and a content quality-based auction;
adjusting, by the data processing system, weightings provided to candidate content items to reflect the normalized ratio between the bid value-based auction and the content quality-based auction, including:
increasing the weightings provided to the candidate content items for the bid value-based auction if the user metric indicates a threshold amount of user activity; and
decreasing the weightings provided to the candidate content items for the bid value-based auction if the user metric indicates less than a threshold amount of user activity;
performing an auction using the parameter to determine selection of content including determining a content item to serve for rendering in a content slot of the resource; and
serving the content item for rendering in the content slot of the resource based on the auction.

2. The method of claim 1 , further comprising:
determining a content ranking of the content based on the adjusted quality score and a bid value associated with the content.

3. The method of claim 2 , further comprising:
determining the content ranking by multiplying the adjusted quality score and the bid value associated with the content.

4. The method of claim 1 , further comprising:
executing the auction as a combination of a bid value based auction and a content quality based auction based on the adjusted quality score.

5. The method of claim 1 , further comprising:
determining the user metric based on at least one of a number of times the computing device has clicked on content during a predetermined time period, a number of impressions displayed by the computing device, or a location of the computing device.

6. The method according to claim 1 , further comprising:
generating the adjusted quality score based on logistic regression analysis.

7. The method according to claim 1 , further comprising:
generating the adjusted quality score by applying a non-linear function to the quality score.

8. The method of claim 1 , wherein the user metric comprises a ratio of a first click-through rate associated with the user and a second click-through rate associated with other users.

9. A computer readable storage medium storing a computer program product which, when executed by a computer, causes the computer to perform functions of:
identifying a quality score of content configured for display with a resource;
identifying a user metric of a computing device based on activity of the user;
generating an adjusted quality score of the content based on the quality score and the user metric; and
selecting a parameter for an auction based on the adjusted quality score, the parameter indicating a normalized ratio between a bid value-based auction and a content quality-based auction;
adjusting weightings provided to candidate content items to reflect the normalized ratio between the bid value-based auction and the content quality-based auction, including:
increasing the weightings provided to the candidate content items for the bid value-based auction if the user metric indicates a threshold amount of user activity; and
decreasing the weightings provided to the candidate content items for the bid value-based auction if the user metric indicates less than a threshold amount of user activity;

performing an auction using the parameter to determine selection of content including determining a content item to serve for rendering in a content slot of the resource; and
serving the content item for rendering in the content slot of the resource based on the auction.

10. The computer readable storage medium of claim 9 , further comprising causing the computer to perform the function of:
determining a content ranking of the content based on the adjusted quality score and a bid value associated with the content.

11. The computer readable storage medium of claim 10 , wherein the content ranking is determined by multiplying the adjusted quality score and the bid value associated with the content.

12. The computer readable storage medium of claim 9 , further comprising causing the computer to perform the function of:
providing the adjusted quality score to an auction computation circuit.

13. The computer readable storage medium of claim 9 , further comprising causing the computer to perform the function of:
executing the auction as a combination of a bid value based auction and a content quality based auction based on the adjusted quality score.

14. The computer readable storage medium of claim 9 , further comprising causing the computer to perform the function of:
determining the user metric based on at least one of a number of times the computing device has clicked on content during a predetermined time period, a number of impressions displayed by the computing device, or a location of the computing device.

15. The computer readable storage medium of claim 9 , wherein the adjusted quality score is generated based on logistic regression analysis.

16. The computer readable storage medium of claim 9 , wherein the adjusted quality score is generated by applying a non-linear function to the quality score.

17. A system comprising:
at least one processor operably coupled to at least one memory and configured to:
identify a quality score of content configured for display with a resource;
identify a user metric of a computing device based on activity of the user;
generate an adjusted quality score of the content based on the quality score and the user metric; and
select a parameter for an auction based on the adjusted quality score, the parameter indicating a normalized ratio between a bid value-based auction and a content quality-based auction;
adjust, by the data processing system, weightings provided to candidate content items to reflect the normalized ratio between the bid value-based auction and the content quality-based auction, including:
increase the weightings provided to the candidate content items for the bid value-based auction if the user metric indicates a threshold amount of user activity; and
decrease the weightings provided to the candidate content items for the bid value-based auction if the user metric indicates less than a threshold amount of user activity;

perform an auction using the parameter to determine selection of content including determining a content item to serve for rendering in a content slot of the resource; and
serve the content item for rendering in the content slot of the resource based on the auction.

18. The system of claim 17 , wherein the at least one processor is further configured to determine a content ranking of the content based on the adjusted quality score and a bid value associated with the content.

Assignees

• Google Llc

Inventors

• Johnson Nicholas
• Lin Dong
• Ponnekanti Shankar R

Classifications

• G06Q30/0275

  Auctions · CPC title

• G06Q30/08

  Auctions · CPC title

• G06Q30/06

  Buying, selling or leasing transactions · CPC title

• G06Q30/0255 Primary

  based on user history · CPC title

• G06Q30/0246 Primary

  Traffic · CPC title