Behavioral targeting system

• US 20070260596A1
• Filed: 03/29/2006
• Published: 11/08/2007
• Est. Priority Date: 03/29/2006
• Status: Active Grant

• Current Assignee: X Corp. (f/k/a Twitter, Inc.) (X Holdings Corp.)
• Original Assignee: Yahoo! Inc. (Apollo Global Management, Inc.)
• Inventors: Yin, Hongfeng, Gupta, Abhinav, Lin, Long-Ji, John, George, Koran, Joshua, Chung, Christina, Frankel, Richard
• Granted Patent: US 8,504,575 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/337 Profile generation, learning or modification
• CPC Class Codes: G06F 16/635 Filtering based on additional data, e.g. user or group profiles
• CPC Class Codes: G06F 16/84 Mapping; Conversion
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation
• CPC Class Codes: G06Q 10/067 Enterprise or organisation modelling
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0255 based on user history

Abstract

A behavioral targeting system determines user profiles from online activity. The system includes a plurality of models that define parameters for determining a user profile score. Event information, which comprises on-line activity of the user, is received at an entity. To generate a user profile score, a model is selected. The model comprises recency, intensity and frequency dimension parameters. The behavioral targeting system generates a user profile score for a target objective, such as brand advertising or direct response advertising. The parameters from the model are applied to generate the user profile score in a category. The behavioral targeting system has application for use in ad serving to on-line users.

Claims

1. A method for determining a user profile from online activity, said method comprising:

storing a plurality of models at an entity, wherein a model defines a plurality of parameters for determining a user profile score;

receiving, at said entity, at least one user event information, wherein said user event information comprises on-line activity between said user and said entity;

selecting a model to generate a user profile score; and

generating at least one user profile score in a category by applying said parameters from said model selected to said user event information.

2. The method as set forth in claim 1, further comprising:

adding at least one new model comprising new parameter and rules so as to provide extensibility;

receiving, at said entity, event information;

selecting said new model to generate a user profile score; and

generating at least one user profile score in a category by applying said new parameters and rules from said new model selected to said user event information.

3. The method as set forth in claim 1, further comprising serving an advertisement to said user based on said user interest score.

4. The method as set forth in claim 1, wherein said parameters comprise recency dimension parameters that provide input to said user interest score based on how recent said user event information occurred.

5. The method as set forth in claim 1, wherein said parameters comprise intensity dimension parameters that provides input to said user interest score based on effectiveness of said user event information to predict user interest in said category.

6. The method as set forth in claim 1, wherein said parameters comprise frequency dimension parameters that provides input to said user interest score based on frequency of occurrence of said user event information.

7. The method as set forth in claim 1, wherein:

selecting a model to generate a user score comprises selecting a model based on a targeting objective; and

generating at least one user interest score comprises generating a user interest score for said targeting object.

8. The method as set forth in claim 7, wherein said targeting objective comprises brand advertising.

9. The method as set forth in claim 7, wherein said targeting objective comprises direct response advertising.

10. A system for determining user interest from online activity, said system comprising:

storage for storing a plurality of models, wherein a model defines a plurality of parameters for determining a user profile score;

at least one server for receiving at least one user event information, wherein said user event information comprises on-line activity of said user, for selecting a model to generate a user profile score, and for generating at least one user profile score in a category by applying said parameters from said model selected to said user event information.

11. The system as set forth in claim 10, wherein said system comprises an extensible system, such that at least one model may be added without requiring change to the existing system.

12. The system as set forth in claim 10, further comprising an advertisement server for serving an advertisement to said user based on said user interest score.

13. The system as set forth in claim 10, wherein said parameters comprise recency dimension parameters that provide input to said user interest score based on how recent said user event information occurred.

14. The system as set forth in claim 10, wherein said parameters comprise intensity dimension parameters that provides input to said user interest score based on effectiveness of said user event information to predict user interest in said category.

15. The system as set forth in claim 10, wherein said parameters comprise frequency dimension parameters that provides input to said user interest score based on frequency of occurrence of said user event information.

16. The system as set forth in claim 10, wherein said server further for generating a user profile score for a target objective.

17. The system as set forth in claim 16, wherein said target objective comprises brand advertising.

18. The system as set forth in claim 16, wherein said target objective comprises direct response advertising.

21. The computer readable medium as set forth in claim 15, wherein said parameters comprise recency dimension parameters that provide input to said user interest score based on how recent said user event information occurred.

19. A computer readable medium comprising a set of instructions which, when executed by a computer, cause the computer to determine a user profile from online activity, said instructions for:

storing a plurality of models at an entity, wherein a model defines a plurality of parameters for determining a user profile score;

receiving, at said entity, at least one user event information, wherein said user event information comprises on-line activity between said user and said entity;

selecting a model to generate a user profile score; and

generating at least one user profile score in a category by applying said parameters from said model selected to said user event information.

20. The computer readable medium as set forth in claim 19, further comprising serving an advertisement to said user based on said user profile score.

22. The computer readable medium as set forth in claim 19, wherein said parameters comprise intensity dimension parameters that provides input to said user interest score based on effectiveness of said user event information to predict user interest in said category.

23. The computer readable medium as set forth in claim 19, wherein said parameters comprise frequency dimension parameters that provides input to said user interest score based on frequency of occurrence of said user event information.

24. The computer readable medium as set forth in claim 19, wherein:

selecting a model to generate a user score comprises selecting a model based on a target objective; and

generating at least one user profile score comprises generating a user profile score for said target objective.