Predicting ad quality

• US 20070156887A1
• Filed: 12/30/2005
• Published: 07/05/2007
• Est. Priority Date: 12/30/2005
• Status: Abandoned Application

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google LLC (Alphabet Inc.)
• Inventors: Pregibon, Daryl, Wright, Daniel, Tang, Diane
• Application Number:
  US11/321,046
• Publication Number:
  US 20070156887A1
• Time in Patent Office: Days
• US Class Current: 709/224
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising

Abstract

A system provides one or more advertisements to users in response to search queries and logs user behavior associated with user selection of the one or more advertisements. The system also logs features associated with selected ones of the one or more advertisements, or associated with the search queries. The system further uses a statistical model and the logged user behavior to estimate quality scores associated with the selected advertisements and aggregates the estimated quality scores. The system predicts the quality of another advertisement using the aggregated quality scores.

Claims

1. A method, comprising:

determining quality values associated with selections of multiple advertisements, the quality values estimating the likelihood that the multiple advertisements are good advertisements;

aggregating the quality values; and

using the aggregated quality values to predict a future likelihood that another advertisement is good.

2. The method of claim 1, wherein a query is associated with each selection of the multiple advertisements.

3. The method of claim 2, further comprising:

aggregating the quality values based on one or more ad or query features associated with the selections of the multiple advertisements.

4. The method of claim 3, wherein each of the one or more ad or query features comprises an identifier associated with an advertiser of each of the multiple advertisements, a keyword that each of the multiple advertisements targets, a word in the query that each of the multiple advertisements did not target, or a word in the query that each of the multiple advertisements did not target but which is similar to a word targeted by each the multiple advertisements.

5. The method of claim 3, wherein aggregating the quality values comprises:

indexing a data structure, based on the one or more ad or query features, to store the quality values.

6. The method of claim 5, further comprising:

receiving a query;

obtaining a set of advertisements that are relevant to the query;

receiving a selection of the other advertisement from the set of advertisements; and

observing one or more second ad or query features associated with the selection of the other advertisement.

7. The method of claim 6, wherein the one or more second ad or query features comprises at least one of an identifier associated with an advertiser of the other advertisement, a keyword that the other advertisement targets, a word in the query that the other advertisement did not target, or a word in the query that the other advertisement did not target but which is similar to a word targeted by the other advertisement.

8. The method of claim 6, wherein using the aggregated quality values to predict a future likelihood that the other advertisement is good comprises:

retrieving at least some of the aggregated quality values from the data structure based on the one or more second ad or query features.

9. A method, comprising:

providing one or more advertisements to users in response to search queries;

logging user behavior associated with user selection of the one or more advertisements;

logging features associated with selected ones of the one or more advertisements, or associated with the search queries;

using a statistical model and the logged user behavior to estimate quality scores associated with the selected advertisements;

aggregating the estimated quality scores; and

predicting the quality of another advertisement using the aggregated quality scores.

10. The method of claim 9, wherein aggregating the estimated quality scores in memory comprises:

indexing a data structure, based on the logged features, to store the estimated quality scores.

11. The method of claim 9, wherein each of the logged features comprises at least one of an identifier associated with a corresponding one of the one or more advertisements, a keyword that a corresponding one of the one or more advertisements targets, words in queries of the search queries that each of the one or more advertisements did not target, and a word in the search queries that each of the one or more advertisements did not target but which is similar to a word targeted by each of the one or more advertisements.

12. A method, comprising:

receiving a search query from a user;

providing a group of advertisements to the user based on the search query;

receiving, from the user, an indication of a selection of an advertisement from the group of advertisements;

logging features associated with the search query or with the selected advertisement;

retrieving past quality scores from memory using the logged features; and

predicting a future quality of the selected advertisement based on the retrieved past quality scores.

13. The method of claim 12, wherein each of the logged features comprises at least one of an identifier associated with the selected advertisement, a keyword that the selected advertisement targets, words in the search query that the selected advertisement did not target, and a word in the search query that the selected advertisement did not target but which is similar to a word targeted by the selected advertisement.

14. The method of claim 12, wherein retrieving the past quality scores from memory comprises:

indexing a data structure in the memory, using the logged features, to retrieve the past quality scores.

15. The method of claim 12, predicting a future quality of the selected advertisement based on the retrieved past quality scores:

applying an algorithm to the retrieved past quality scores to provide a value that predicts the future quality of the advertisement.

16. A system, comprising:

means for providing multiple advertisements to a user based on a search query issued by the user;

means for receiving, from the user, an indication of a selection of an advertisement from the multiple advertisements;

means for logging features associated with the search query or with the selected advertisement;

means for retrieving past quality scores from a data structure using the logged features; and

means for predicting a quality of the selected advertisement based on the retrieved past quality scores.

17. A computer-readable medium that stores computer-executable instructions, comprising:

instructions for determining quality values associated with multiple selections of an advertisement, each of the quality values estimating the likelihood that the advertisement is a good advertisement in a first context;

instructions for storing the quality values; and

using the stored quality values to predict a future likelihood that the advertisement is good in a second context, where the second context is different than the first context.