Ad Relevance In Sponsored Search

• US 20110270672A1
• Filed: 04/28/2010
• Published: 11/03/2011
• Est. Priority Date: 04/28/2010
• Status: Abandoned Application

• Current Assignee: Excalibur IP, LLC (Acacia Research Corporation)
• Original Assignee: Excalibur IP, LLC (Acacia Research Corporation)
• Inventors: Raghavan, Hema, Manavoglu, Eren, Leggetter, Chris, Hillard, Dustin, Schroedl, Stefan
• Application Number:
  US12/769,446
• Publication Number:
  US 20110270672A1
• 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 30/0243 Comparative campaigns
• CPC Class Codes: G06Q 30/0246 Traffic

Abstract

Techniques for improving advertisement relevance for sponsored search advertising. The method includes steps for processing a click history data structure containing at least a plurality of query-advertisement pairs, populating a first translation table containing a co-occurrence count field, populating a second translation table containing an expected clicks field, and calculating a click propensity score for an advertisement using the click history data structure, the first translation table (for determining overall click likelihood across all historical traffic), and using the second translation table (for removing biases present in the first translation table). Other method steps calculate a second click propensity score for a second advertisement, then ranking the first advertisement relative to the second advertisement for comparing a click propensity score to a threshold for filtering low quality ad candidates from a plurality of ad candidates, and then ranking advertisements for optimizing placement of ads on a sponsored search display page.

Claims

1. A computer-implemented method for improving advertisement relevance for sponsored search advertising comprising:

storing, in a computer memory, a click history data structure for comprising at least a plurality of query-advertisement pairs;

populating a first translation table, in a computer memory, said first translation table comprising a co-occurrence count field;

populating a second translation table, in a computer memory, said second translation table comprising an expected clicks field; and

calculating, at a server, a first click propensity score for a first advertisement using the first translation table, and the second translation table.

2. The method of claim 1, further comprising:

calculating, at a server, a second click propensity score for a second advertisement using the first translation table, and the second translation table; and

ranking, at a server, at least the first advertisement and the second advertisement based on the first click propensity score and the second click propensity score.

3. The method of claim 1, further comprising:

comparing the first click propensity score to a threshold for filtering low quality ad candidates from a plurality of ad candidates.

4. The method of claim 1, further comprising:

comparing the first click propensity score the second click propensity score for ordering ads on a sponsored search display page.

5. The method of claim 1, further comprising:

comparing the first click propensity score the second click propensity score for optimizing placement of ads on a sponsored search display page.

6. The method of claim 1, wherein the populating the first translation table includes calculating based machine learning estimation of a co-occurrences between a query and an advertisement.

7. The method of claim 1, wherein the populating the second translation table includes calculating based on a ranked position of an advertisement.

8. The method of claim 1, wherein the relevance model contains at least one of a query length, title, an ad description, a display URL.

9. An advertising server network for improving advertisement relevance for sponsored search advertising comprising:

a module for storing, in a computer memory, a click history data structure for comprising at least a plurality of query-advertisement pairs;

a module for populating a first translation table, in a computer memory, said first translation table comprising a co-occurrence count field;

a module for populating a second translation table, in a computer memory, said second translation table comprising an expected clicks field; and

a module for calculating, at a server, a first click propensity score for a first advertisement using the first translation table, and the second translation table.

10. The advertising server network of claim 9, further comprising:

a module for calculating, at a server, a second click propensity score for a second advertisement using the first translation table, and the second translation table; and

a module for ranking, at a server, at least the first advertisement and the second advertisement based on the first click propensity score and the second click propensity score.

11. The advertising server network of claim 9, further comprising:

comparing the first click propensity score to a threshold for filtering low quality ad candidates from a plurality of ad candidates.

12. The advertising server network of claim 9, further comprising:

comparing the first click propensity score the second click propensity score for ordering ads on a sponsored search display page.

13. The advertising server network of claim 9, further comprising:

comparing the first click propensity score the second click propensity score for optimizing placement of ads on a sponsored search display page.

14. The advertising server network of claim 9, wherein the populating the first translation table includes calculating based maximum likelihood estimation of a co-occurrences between a query and an advertisement.

15. The advertising server network of claim 9, wherein the populating the second translation table includes calculating based on a ranked position of an advertisement.

16. The advertising server network of claim 9, wherein the relevance model contains at least one of a query length, title, an ad description, a display URL.

17. A computer readable medium comprising a set of instructions which, when executed by a computer, cause the computer to improve advertisement relevance for sponsored search advertising comprising, the set of instructions for:

storing, in a computer memory, a click history data structure for comprising at least a plurality of query-advertisement pairs;

populating a first translation table, in a computer memory, said first translation table comprising a co-occurrence count field;

populating a second translation table, in a computer memory, said second translation table comprising an expected clicks field; and

calculating, at a server, a first click propensity score for a first advertisement using the first translation table, and the second translation table.

18. The computer readable medium of claim 17, further comprising:

calculating, at a server, a second click propensity score for a second advertisement using the first translation table, and the second translation table; and

ranking, at a server, at least the first advertisement and the second advertisement based on the first click propensity score and the second click propensity score.

19. The computer readable medium of claim 17, further comprising:

comparing the first click propensity score to a threshold for filtering low quality ad candidates from a plurality of ad candidates.

20. The computer readable medium of claim 17, further comprising:

comparing the first click propensity score the second click propensity score for ordering ads on a sponsored search display page.