AD PLACEMENT

• US 20130054347A1
• Filed: 09/12/2012
• Published: 02/28/2013
• Est. Priority Date: 11/08/1999
• Status: Active Grant

• Current Assignee: Meta Platforms, Inc. (f/k/a Facebook, Inc.)
• Original Assignee: Meta Platforms, Inc. (f/k/a Facebook, Inc.)
• Inventors: Ferber, John B., Ferber, Scott, Kretsinger, Stein E., Luenberger, Robert, Luenberger, David
• Granted Patent: US 10,217,128 B2
• Time in Patent Office: Days
• US Class Current: 705/14.42
• 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. (canceled)

2. A method comprising:

selecting, using at least one processor, one or more non-banner advertisements of a plurality of non-banner advertisements using a first selection heuristic;

selecting, using the at least one processor, one or more banner advertisements of a plurality of banner advertisements using a second selection heuristic, wherein the second selection heuristic differs from the first selection heuristic;

serving the one or more non-banner advertisements over a communications network; and

serving the one or more banner advertisements over the communications network.

3. The method as recited in claim 2, further comprising determining an estimated selection probability for each non-banner advertisement of the plurality of non-banner advertisements, the estimated selection probability indicating a likelihood that a user will select a given non-banner advertisement.

4. The method as recited in claim 3, wherein selecting one or more non-banner advertisements comprises selecting the non-banner advertisement with a high estimated probability.

5. The method as recited in claim 3, further comprising maintaining a user profile, the user profile including user attributes reflecting interests of a user.

6. The method as recited in claim 5, further comprising updating the user attributes of the user profile based on one or more of Internet sites visited by the user, advertisements selected by the user, or internet searching by the user.

7. The method as recited in claim 6, further comprising maintaining an advertisement profile for one or more advertisements of the plurality of non-banner advertisements, each advertisement profile including ad-attributes that reflect how much an advertisement correlates to a given user attribute.

8. The method as recited in claim 7, wherein the estimated selection probability for a particular non-banner advertisement is a function of the consumer attributes of a given user and the ad-attributes for the particular non-banner advertisement.

9. The method as recited in claim 2, wherein the second selection heuristic comprises a principal component analysis.

10. The method as recited in claim 2, further comprising serving the one or more non-banner advertisements to a marketing medium.

11. The method as recited in claim 10, wherein the marketing medium is on a mobile device.

12. The method as recited in claim 10, wherein the marketing medium comprises a software program.

13. The method as recited in claim 10, wherein the marketing medium comprises an Internet browser.

14. A non-transitory computer-readable storage medium including a set of instructions that, when executed, cause at least one processor to perform steps comprising:

selecting one or more non-banner advertisements of a plurality of non-banner advertisements using a first selection heuristic;

selecting one or more banner advertisements of a plurality of banner advertisements using a second selection heuristic, wherein the second selection heuristic differs from the first selection heuristic;

serving the one or more non-banner advertisements over a communications network; and

serving the one or more banner advertisements over the communications network.

15. The computer-readable storage medium as recited in claim 14, further comprising instructions that, when executed, cause the at least one processor to determine an estimated selection probability for each non-banner advertisement of the plurality of non-banner advertisements, the estimated selection probability indicating a likelihood that a user will select a given non-banner advertisement.

16. The computer-readable storage medium as recited in claim 15, wherein selecting one or more non-banner advertisements comprises selecting the non-banner advertisement with a high estimated probability.

17. The computer-readable storage medium as recited in claim 15, further comprising instructions that, when executed, cause the at least one processor to maintain a user profile, the user profile including user attributes reflecting interests of a user.

18. The computer-readable storage medium as recited in claim 17, further comprising instructions that, when executed, cause the at least one processor to update the user attributes of the user profile based on one or more of Internet sites visited by the user, advertisements selected by the user, or internet searching by the user.

19. The computer-readable storage medium as recited in claim 18, further comprising instructions that, when executed, cause the at least one processor to maintain an advertisement profile for one or more advertisements of the plurality of non-banner advertisements, each advertisement profile including ad-attributes that reflect how much an advertisement correlates to a given user attribute.

20. The computer-readable storage medium as recited in claim 19, wherein the estimated selection probability for a particular non-banner advertisement is a function of the consumer attributes of a given user and the ad-attributes for the particular non-banner advertisement.

21. The computer-readable storage medium as recited in claim 14, wherein the second selection heuristic comprises a principal component analysis.

22. The computer-readable storage medium as recited in claim 14, further comprising instructions that, when executed, cause the at least one processor to serve the one or more non-banner advertisements to a marketing medium.

23. The computer-readable storage medium as recited in claim 22, wherein the marketing medium is on a mobile device.

24. The computer-readable storage medium as recited in claim 22, wherein the marketing medium comprises a software program.

25. The computer-readable storage medium as recited in claim 22, wherein the marketing medium comprises an Internet browser.

26. A method comprising:

determining, using at least one processor, an estimated selection probability for one or more advertisements of a plurality of advertisements, wherein;

the estimated selection probability indicates a likelihood that the user will select a given advertisement; and

determining the estimated selection probability comprises using a principal component analysis; and

serving an advertisement with a high estimated selection probability.

27. The method as recited in claim 26, wherein determining the estimated selection probability further comprises calculating a click-thru-rate for the one or more advertisements of the plurality of advertisements.

28. The method as recited in claim 26, further comprising binomially updating the estimated selection probability of the one or more advertisements of the plurality of advertisements.

29. The method as recited in claim 28, further comprising:

maintaining a user profile, the user profile including user attributes reflecting interests of the user; and

maintaining an advertisement profile for the one or more advertisements of the plurality of advertisements, each advertisement profile indicating how much a given advertisement correlates to a given user attribute;

wherein the estimated selection probability for a particular advertisement is a function of the consumer attributes of a given user and the advertisement profile for the particular advertisement.

30. The method as recited in claim 26, wherein serving an advertisement with a high estimated selection probability comprises serving the advertisement with the highest estimated selection probability.

31. The method as recited in claim 26, further comprising serving the advertisement with a high estimated selection probability to a marketing medium.

32. The method as recited in claim 31, wherein the marketing medium is on a mobile device.

33. The method as recited in claim 32, wherein the marketing medium comprises a software program.

34. The method as recited in claim 32, wherein the marketing medium comprises an Internet browser.

35. A method comprising:

selecting, using at least one processor, one or more advertisements of a first advertisement type using a first selection heuristic;

selecting, using the at least one processor, one or more advertisements of a second advertisement type using a second selection heuristic, wherein the second selection heuristic differs from the first selection heuristic;

serving the one or more advertisements of the first advertisement type to one or more mobile devices; and

serving the one or more advertisements of the second advertisement type to one or more mobile devices.

36. The method as recited in claim 35, further comprising determining an estimated selection probability for each advertisement of the first advertisement type, the estimated selection probability indicating a likelihood that a user will select a given advertisement of the first advertisement type.

37. The method as recited in claim 36, wherein selecting one or more advertisements of the first advertisement type comprises selecting an advertisement with a high estimated probability.

38. The method as recited in claim 36, further comprising maintaining a user profile, the user profile including user attributes reflecting interests of a user.

39. The method as recited in claim 38, further comprising updating the user attributes of the user profile based on one or more of Internet sites visited by the user, advertisements selected by the user, internet searching by the user, or activities of the user on a marketing medium.

40. The method as recited in claim 39, further comprising maintaining an advertisement profile for one or more advertisements of the first advertisement type, each advertisement profile including ad-attributes that reflect how much an advertisement correlates to a given user attribute.

41. The method as recited in claim 40, wherein the estimated selection probability for a particular advertisement of the first advertisement type is a function of the consumer attributes of a given user and the ad-attributes for the particular advertisement.

42. The method as recited in claim 35, wherein the second selection heuristic comprises a principal component analysis.

43. The method as recited in claim 35, further comprising serving the one or more advertisements of the first advertisement type to a marketing medium.

44. The method as recited in claim 43, wherein the marketing medium comprises a software program.

45. The method as recited in claim 43, wherein the marketing medium comprises an Internet browser.