Automatic ad placement

• US 20060293950A1
• Filed: 06/28/2005
• Published: 12/28/2006
• Est. Priority Date: 06/28/2005
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Meek, Christopher A., Chickering, David Maxwell, Heckerman, David Earl
• Application Number:
  US11/168,149
• Publication Number:
  US 20060293950A1
• Time in Patent Office: Days
• US Class Current: 705/14.52
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0254 based on statistics
• CPC Class Codes: G06Q 30/08 Auctions

Abstract

A computer-implemented method is provided for controlling placement of ad impressions, corresponding to ads, displayed on a web page. The method includes recording features corresponding to ad impressions. Recording features can include collecting sufficient statistics for a Naïve Bayes model in some embodiments. A statistical algorithm is then used to automatically control placement of ad impressions.

Claims

1. A computer-implemented method for controlling placement of ad impressions, corresponding to ads, displayed on a web page, the method comprising:

recording features corresponding to each of a plurality of clicked on ad impressions;

recording features for a random sample of ad impressions;

using a statistical algorithm to predict click through rates; and

automatically controlling placement of ad impressions based upon the prediction of click-through rates.

2. The computer-implemented method of claim 1, wherein using the statistical algorithm to predict click through rates further comprises:

automatically using the statistical algorithm at regular intervals to update the identification of which features are most predictive of click through rates.

3. The computer-implemented method of claim 2, wherein automatically using the statistical algorithm at regular intervals further comprises:

automatically using the statistical algorithm at least once a day to update the identification of which features are most predictive of click through rates.

4. The computer-implemented method of claim 2, wherein using the statistical algorithm predict click through rates further comprises:

using the statistical algorithm to identify click through rates for each individual ad.

5. The computer-implemented method of claim 4, wherein automatically controlling placement of ad impressions further comprises:

automatically controlling, for each individual ad, which user demographic type the corresponding ad impressions are shown to.

6. The computer-implemented method of claim 4, wherein automatically controlling placement of ad impressions further comprises:

automatically controlling times, for each individual ad, that the corresponding ad impressions are shown.

7. The computer-implemented method of claim 4, wherein automatically controlling placement of ad impressions further comprises:

automatically controlling, for each individual ad, placement positions of the corresponding ad impressions on web pages.

8. The computer-implemented method of claim 1, wherein automatically controlling placement of ad impressions further comprises:

automatically controlling placement of ad impressions based upon the prediction of click-through rates in a particular context.

9. The computer-implemented method of claim 8, wherein the particular context includes a keyword or phrase bought by an advertiser.

10. The computer-implemented method of claim 8, wherein the particular context includes a search phrase issued by the web site user.

11. A computer-readable medium containing computer-executable instructions for implementing the steps of claim 1.

12. An ad serving system configured to execute computer-executable instructions for implementing the steps of claim 1.

13. A computer-implemented method for controlling placement of ad impressions, corresponding to ads, displayed on a web page, the method comprising:

collecting sufficient statistics for a Naï

e Bayes model for each of a plurality of ad impressions, a first portion of the plurality of ad impressions having been clicked on, and a second portion of the plurality of ad impressions having not been clicked on;

using a Naï

e Bayes model, with the sufficient statistics for the Naï

e Bayes model, to predict click through rates for ad impressions corresponding to ads;

automatically controlling placement of ad impressions based on the predicted click through rates.

14. The computer-implemented method of claim 13, wherein collecting the sufficient statistics for the Naï

e Bayes model further comprises collecting paired counts for a plurality of features, the paired counts for each feature representing for a particular person whether the feature was true and the particular person clicked on the ad impression, or whether the feature was true and the particular person did not click on the ad impression.

15. The computer-implemented method of claim 14, wherein each of the plurality of features has discrete values.

16. The computer-implemented method of claim 13, wherein using the Naï

e Bayes model to predict click through rates for ad impressions corresponding to ads further comprises;

automatically using the Naï

e Bayes model at predetermined intervals to predict click through rates for ad impressions corresponding to ads.

17. The computer-implemented method of claim 16, wherein automatically controlling placement of ad impressions based on the predicted click through rates further comprises:

automatically controlling times, for each individual ad, that the corresponding ad impressions are shown.

18. The computer-implemented method of claim 16, wherein automatically controlling placement of ad impressions based on the predicted click through rates further comprises:

automatically controlling, for each individual ad, placement positions of the corresponding ad impressions on web pages.

19. A computer-readable medium containing computer-executable instructions for implementing the steps of claim 13.

20. An ad serving system configured to execute computer-executable instructions for implementing the steps of claim 13.