METHOD AND SYSTEM FOR QUANTIFYING USER INTERACTIONS WITH WEB ADVERTISEMENTS

• US 20100217648A1
• Filed: 02/20/2009
• Published: 08/26/2010
• Est. Priority Date: 02/20/2009
• Status: Active Grant

• Current Assignee: R2 Solutions LLC (Acacia Research Corporation)
• Original Assignee: Yahoo! Inc. (Apollo Global Management, Inc.)
• Inventors: Hall, Robert, Josifovski, Vanja, Gabrilovich, Evgeniy, Agarwal, Deepak K., Broder, Andrei
• Granted Patent: US 8,812,362 B2
• Time in Patent Office: Days
• US Class Current: 705/10
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities

Abstract

Methods and systems are provided that may be used to determine a probability of whether a visitor to a web document is likely to click on a web advertisement. An exemplary method may include detecting one or more features in a web document. One or more expert statistical models to which the web document belongs may be determined and associated weightings may be determined based, at least in part, on the one or more features detected. A click-through-rate probability for a web advertisement to be placed on the web document may be estimated based on the one or more expert statistical models.

Claims

1. A method comprising:

executing instructions on a specific apparatus to;

determine one or more features in a web document;

select one or more expert statistical models to which the web document belongs and determine weightings for the one or more expert statistical models based, at least in part, on one or more relevance scores for the one or more features in the web document; and

estimate a click-through-rate probability for a web advertisement to be placed on the web document based on the weightings for the one or more expert statistical models.

2. The method of claim 1, wherein the web document comprises at least one of a web page or a search query.

3. The method of claim 1, wherein the determining weightings for one or more expert statistical models to which the web document belongs comprises assigning a particular weighting to a particular expert statistical model based on a relevance score between the web document and the particular expert statistical model.

4. The method of claim 3, wherein the relevance score between the web document and the particular expert statistical model is based, at least in part, on the one or more features selected.

5. The method of claim 1, further comprising determining expected revenue for the web advertisement based on the click-through-rate probability and an auction bid corresponding to the web advertisement.

6. The method of claim 1, wherein the estimating the click-through-rate probability is based, at least in part, on at least one of a publisher identifier corresponding to a publisher of the web document, a predetermined position in which the publisher locates web advertisements on the web document, or a relevance score based on a comparison of the one or more features in the web document and one or more features of the web advertisement.

7. The method of claim 1, wherein the one or more features comprise at least one of key words located in the web document, phrases detected in the web document, and taxonomy of content in the web document.

8. The method of claim 1, wherein the determining weightings for one or more expert statistical models to which the web document belongs comprises assigning weightings to the one or more features detected in the web document, and selecting the one or more expert statistical models based on combined weightings of the one or more features.

9. An article comprising:

a storage medium comprising machine-readable instructions stored thereon which, if executed by a specific apparatus, are adapted to enable the specific apparatus to;

detect one or more features in a web document;

select one or more expert statistical models to which the web document belongs and determine weightings for one or more expert statistical models based, at least in part, on one or more relevance scores for the one or more features detected; and

estimate, based on the weightings for the one or more expert statistical models, a click-through-rate probability for a web advertisement to be placed on the web document.

10. The article of claim 9, wherein the machine-readable instructions are further adapted to enable the specific apparatus to determine a set of expert statistical models to predict the click-through-rate probability for web documents and web advertisements based on an analysis of the one or more features in the web documents and the web advertisements.

11. The article of claim 9, wherein the machine-readable instructions are further adapted to enable the specific apparatus to select the one or more expert statistical models from a plurality of expert statistical models based, at least in part, on the one or more features selected.

12. The article of claim 9, wherein the machine-readable instructions are further adapted to enable the specific apparatus to estimate the click-through-rate probability based, at least in part, on at least one of a publisher identifier corresponding to a publisher of the web document, a predetermined position in which the publisher locates one or more web advertisements on the web document, or a relevance score based on a comparison of the one or more features in the web document and one or more features of the web advertisement.

13. The article of claim 9, wherein the machine-readable instructions are further adapted to enable the specific apparatus to assign weights to one or more features detected in the web document, and select and determined weightings for the one or more expert statistical models based on combined weights of the one or more features.

14. A system comprising:

a specific apparatus adapted to;

detect one or more features in a web document;

select one or more expert statistical models to which the web document belongs based and determine weightings for one or more expert statistical models, at least in part, on one or more relevance scores for the one or more features detected; and

estimate, based on the weightings for the one or more expert statistical models, a click-through-rate probability for a web advertisement to be placed on the web document.

15. The system of claim 14, wherein the web document comprises at least one of a web page or a search query.

16. The system of claim 14, wherein the specific apparatus is adapted to determine a set of expert statistical models to predict the click-through-rate probability for web documents and web advertisements based on an analysis of the one or more features in the web documents and the web advertisements.

17. The system of claim 14, wherein the specific apparatus is further adapted to determine expected revenue for the web advertisement based on the click-through-rate probability and an auction bid corresponding to the web advertisement.

18. The system of claim 14, wherein the specific apparatus is further adapted to estimate the click-through-rate probability based, at least in part, on at least one of a publisher identifier corresponding to a publisher of the web document, a predetermined position in which the publisher locates web advertisements on the web document, or a relevance score based on a comparison of the one or more features in the web document and one or more features of the web advertisement.

19. The system of claim 14, wherein the one or more features comprise at least one of key words located in the web document, phrases detected in the web document, and taxonomy of content in the web document.