Systems and methods for placing electronic advertisements

• US 20100257053A1
• Filed: 02/04/2010
• Published: 10/07/2010
• Est. Priority Date: 11/08/1999
• Status: Active Grant

• Current Assignee: Meta Platforms, Inc. (f/k/a Facebook, Inc.)
• Original Assignee: AOL Advertising Incorporated (Apollo Global Management, Inc.)
• Inventors: Ferber, Scott, Luenberger, David, Ferber, John B., Kretsinger, Stein E., Luenberger, Robert
• Granted Patent: US 9,754,282 B2
• Time in Patent Office: Days
• US Class Current: 705/14.450
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0241 Advertisements
• CPC Class Codes: G06Q 30/0244 Optimization
• CPC Class Codes: G06Q 30/0246 Traffic
• CPC Class Codes: G06Q 30/0251 Targeted advertisements
• CPC Class Codes: G06Q 30/0254 based on statistics
• CPC Class Codes: G06Q 30/0269 based on user profile or attribute
• CPC Class Codes: G06Q 30/0277 Online advertisement

Abstract

This invention concerns optimal ad selection for Web pages by selecting and updating an attribute set, obtaining and updating an ad-attribute profile, and optimally choosing the next ad. The present invention associates a set of attributes with each customer. The attributes reflect the customers'"'"' interests and they incorporate the characteristics that impact ad selection. Similarly, the present invention associates with each ad an ad-attribute profile in order to calculate a customer'"'"'s estimated ad selection probability and measure the uncertainty in that estimate. An ad selection algorithm optimally selects which ad to show based on the click probability estimates and the uncertainties regarding these estimates.

Claims

1-9. -9. (canceled)

10. A computer-implemented method for optimizing placement of Internet advertisements, comprising:

establishing a customer profile for a customer, the customer profile including category attributes related to a category of advertisements of interest to the customer and product attributes related to a product of interest to the customer;

receiving an Internet advertisement;

computing a click probability estimate representing a likelihood that the customer will select the Internet advertisement according to the category attributes and the product attributes;

determining a strength of the click probability estimate based on a number of times the Internet advertisement has been previously presented;

causing the Internet advertisement to be presented to the customer based on the click probability estimate and the strength of the click probability estimate;

receiving a response to the Internet advertisement from the customer;

adjusting the click probability estimate for the Internet advertisement based on the received response to the Internet advertisement; and

increasing the strength of the click probability estimate based on the presentation of the Internet advertisement.

11. The computer-implemented method of claim 10, further comprising:

adjusting the customer profile based on types of advertisements previously responded to by the customer.

12. The computer-implemented method of claim 10, further comprising:

adjusting the customer profile based on Internet sites visited by the customer.

13. The computer-implemented method of claim 10, wherein the strength of the click probability estimate represents an uncertainty of the click probability estimate.

14. The computer-implemented method of claim 13, further comprising:

increasing the strength of the click probability estimate by reducing the uncertainty of the click probability estimate.

15. The computer-implemented method of claim 10, wherein the response is a click selecting the Internet advertisement.

16. The computer-implemented method of claim 10, wherein the response is a request for more information.

17. The computer-implemented method of claim 10, wherein the response is a purchase of an item.

18. A computer-implemented method for optimizing placement of electronic advertisements, comprising:

establishing a customer profile for a customer, the customer profile including at least category attributes related to a category of advertisements of interest to the customer;

computing a click probability estimate representing a likelihood that the customer will select an electronic advertisement;

determining a strength of the click probability estimate based on a number of times the electronic advertisement has been previously presented; and

causing the electronic advertisement to be presented to the customer based on the click probability estimate and the strength of the click probability estimate.

19. The computer-implemented method of claim 18, further comprising:

receiving a response to the electronic advertisement from the customer;

adjusting the click probability estimate for the electronic advertisement based on the received response to the electronic advertisement; and

increasing the strength of the click probability estimate based on the presentation of the electronic advertisement.

20. The computer-implemented method of claim 18, further comprising:

adjusting the customer profile based on types of electronic advertisements previously responded to by the customer.

21. The computer-implemented method of claim 18, further comprising:

adjusting the customer profile based on Internet sites visited by the customer.

22. The computer-implemented method of claim 18, wherein the strength of the click probability estimate represents an uncertainty of the click probability estimate.

23. The computer-implemented method of claim 22, further comprising:

increasing the strength of the click probability estimate by reducing the uncertainty of the click probability estimate.

24. The computer-implemented method of claim 19, wherein the response is a click selecting the electronic advertisement.

25. The computer-implemented method of claim 19, wherein the response is a request for more information.

26. The computer-implemented method of claim 19, wherein the response is a purchase of an item.

27. A computer-implemented method for optimizing placement of electronic advertisements, comprising:

establishing a customer profile for a customer, the customer profile including category attributes related to a category of advertisements of interest to the customer and product attributes related to a product of interest to the customer;

computing a click probability estimate representing a likelihood that the customer will select an electronic advertisement according to the category attributes and the product attributes;

causing the electronic advertisement to the customer based on the click probability estimate;

receiving a response to the Internet advertisement from the customer;

adjusting the click probability estimate for the Internet advertisement based on the received response to the Internet advertisement; and

adjusting the customer profile based on types of advertisements previously responded to by the customer, and Internet sites visited by the customer.