ONLINE ADVERTISING VALUATION APPARATUS AND METHOD

• US 20070288503A1
• Filed: 06/14/2007
• Published: 12/13/2007
• Est. Priority Date: 08/08/2005
• Status: Active Grant

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: Taylor, David
• Granted Patent: US 8,027,876 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0242 Determining effectiveness of advertisements
• CPC Class Codes: G06Q 30/0248 Avoiding fraud
• 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: Y10S 707/99943 Generating database or data structure, e.g. via user interface

Abstract

A method comprising for determining the value provided to an advertiser through the advertiser'"'"'s online advertisements. The method may begin with providing an online advertisement linked to content. A macro-context may be obtained that quantifies the affinity of the content for a plurality of domains corresponding to a semantic space. A personalization vector may be obtained that quantifies the affinity of at least one aspect of a user for the plurality of domains. The user may then select the online advertisement. A match value may be calculated that quantifies the similarity between the personalization vector and the macro-context. The advertiser may then be charged a monetary amount for the user'"'"'s “click.” The monetary amount may be based, at least in part, on the match value.

Claims

1. A method comprising:

providing an online advertisement linked to content;

obtaining a macro-context quantifying the affinity of the content for a plurality of domains corresponding to a semantic space;

obtaining a personalization vector quantifying the affinity of at least one aspect of a user for the plurality of domains;

selecting, by the user, the online advertisement; and

calculating a match value quantifying the similarity between the personalization vector and the macro-context.

2. The method of claim 1, further comprising selecting a match threshold corresponding to a particular level of similarity between the personalization vector and the macro-context.

3. The method of claim 2, wherein the match threshold is selected by an advertiser associated with the online advertisement.

4. The method of claim 2, further comprising billing the advertiser a first amount when the match value is less than the match threshold.

5. The method of claim 4, further comprising billing the advertiser a second amount when the match value is more than the match threshold.

6. The method of claim 5, wherein the second amount is greater than the first amount.

7. The method of claim 6, wherein calculating the match value begins upon selection by the user of the online advertisement.

8. The method of claim 7, wherein the personalization vector is derived at least in part from an online search and click history of the user.

9. The method of claim 8, wherein calculating a match value comprises multiplying together the macro-context and the personalization vector.

10. The method of claim 9, wherein multiplying comprises using one of using a dot product and a weighted dot product.

11. The method of claim 1, further comprising billing an advertiser associated with the online advertisement a greater amount when the match value is above a match threshold.

12. The method of claim 1, further comprising billing an advertiser associated with the online advertisement a greater amount when the match value is above a match threshold selected by the advertiser.

13. The method of claim 1, wherein calculating the match value begins upon selection by the user of the online advertisement.

14. The method of claim 1, wherein the personalization vector is derived at least in part from an online search and click history of the user.

15. The method of claim 1, wherein calculating a match value comprises multiplying together the macro-context and the personalization vector.

16. The method of claim 1, wherein calculating a match value comprises multiplying together the macro-context and the personalization vector using one of a dot product and a weighted dot product.

17. A method comprising:

providing an online advertisement linked to content;

obtaining a first vector containing numerical values quantifying the affinity of the content for each domain of a plurality of domains corresponding to a semantic space;

obtaining a second vector containing numerical values quantifying the affinity of at least one aspect of a user for each domain of the plurality of domains;

selecting, by the user, the online advertisement; and

calculating a match value quantifying the similarity between the first vector and the second vector.

18. The method of claim 17, wherein the second vector is derived at least in part from a third vector corresponding to a first website browsed by the user, the third vector containing numerical values quantifying the affinity of the first website for each domain of the plurality of domains.

19. The method of claim 18, wherein the second vector is derived at least in part from a fourth vector corresponding to a second website browsed by the user, the forth vector containing numerical values quantifying the affinity of the second website for each domain of the plurality of domains.

20. A method comprising:

providing an online advertisement linked to content;

obtaining a first vector containing numerical values quantifying the affinity of the content for each domain of a plurality of domains corresponding to a semantic space;

obtaining a second vector containing numerical values quantifying the affinity of at least one aspect of a user for each domain of the plurality of domains;

selecting, by the user, the online advertisement;

calculating a match value quantifying the similarity between the first vector and the second vector;

identifying an advertiser associated with the online advertisement; and

considering the match value in calculating the monetary amount charged the advertiser for the selection of the online advertisement.