Empirical validation of suggested alternative queries

• US 20060230005A1
• Filed: 03/30/2005
• Published: 10/12/2006
• Est. Priority Date: 03/30/2005
• Status: Abandoned Application

• Current Assignee: Google Inc. (Alphabet Inc.)
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: Bailey, David R., Battle, Alexis J., Nayak, P. Pandurang, Gomes, Benedict A.
• Application Number:
  US11/095,920
• Publication Number:
  US 20060230005A1
• Time in Patent Office: Days
• US Class Current: 706/12
• CPC Class Codes: G06F 16/3338 Query expansion
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06F 16/9532 Query formulation

Abstract

An information retrieval system includes a query revision architecture that integrates multiple different query revisers, each implementing one or more query revision strategies. A revision server receives a user'"'"'s query, and interfaces with the various query revisers, each of which generates one or more potential revised queries. The revision server evaluates the potential revised queries, and selects one or more of them to provide to the user. A confidence estimator and method provide the ability to improve the likelihood of success of suggested revised queries derived from various revision strategies. This is accomplished by tracking user queries, query revision links, results associated with revised queries, and various features of the original query and revised queries. This data is then analyzed using a predictive model to generate a set of rules that can be used to estimate the likelihood of a revised query being a successful revision for a given query.

Claims

1. A method for improving the likelihood of success of suggested revised queries for original queries, where the revised queries are generated from the original queries using one or more revision strategies, the method comprising:

maintaining log files of user clicks on revised queries associated with an original query;

generating, using the log files, a predictive model to estimate a likelihood of success of the revised queries with respect to the original queries; and

applying the original query and the revised queries to the predictive model to obtain scores for the revised queries.

2. The method of claim 1, wherein the log files include features associated with the original query and the revised queries and the predictive model generates a series of rules using the features.

3. The method of claim 2, wherein the features include at least one from the group consisting of the original query, each word in original query, the length of original query, a topic duster of the original query, an information retrieval score for the original query, and the number of results for the original query.

4. The method of claim 2, wherein the features include at least one from the group consisting of one of the revised queries, each word in the one of the revised queries, an identification of the revision technique that generated the one of the revised queries, the length of the one of the revised queries, a topic cluster associated with the one of the revised queries, an information retrieval score for a top search result for the one of the revised queries, the number of results found for the one of the revised queries, the length of a click on a link for the one of the revised queries, and the length of a click on results for the one of the revised queries.

5. The method of claim 1, wherein generating the predictive model to estimate a likelihood of success of the revised queries comprises:

selecting features associated with the revised queries;

collecting click data from the log files;

formulating a rule using the features and the click data; and

adding the rule to the predictive model.

6. The method of claim 5, further comprising training the predictive model, comprising:

formulating additional rules using the click data; and

selectively adding the additional rules to the predictive model.

7. The method of claim 5 or 6, wherein the click data includes click length data.

8. The method of claim 7, wherein the click length data is associated with the revised queries or corresponding search results.

9. The method of claim 1, wherein the prediction measures serve as confidence measures for ranking the revised queries.

10. The method of claim 1, further comprising sorting the revised queries by the prediction measures.

11. A method for improving the likelihood of success of suggested revised queries, comprising:

maintaining log files of user clicks on a list of revised queries associated with an original query;

selecting features associated with the revised queries;

collecting click data from the log files;

formulating a rule using the features and the click data;

using the rule to generate a predictive model to estimate a likelihood of success of the revised queries;

applying the original query and the revised queries to the predictive model to obtain prediction measures for the revised queries; and

sorting the revised queries by the prediction measures.

12. The method of claim 11, further comprising training the predictive model, comprising:

formulating additional rules using the click data; and

selectively adding the additional rules to the predictive model.

13. The method of claim 11, wherein the prediction measures serve as confidence measures for ranking the revised queries.

14. The method of claim 11, further comprising sorting the revised queries by the prediction measures.

15. A method for improving the likelihood of success of suggested revised queries, comprising:

maintaining log files of user clicks on a list of revised queries associated with an original query, the log files including features associated with the original query and the revised queries;

selecting a subset of features associated with the revised queries;

collecting click data from the log files, the click data including click length data, wherein longer clicks indicate greater user satisfaction;

formulating a rule using the subset of features and the click data;

using the rule to generate a predictive model to estimate a likelihood of success of the revised queries, the predictive model including a series of rules using the subset of the features;

training the predictive model, comprising;

formulating additional rules using the click data; and

selectively adding the additional rules to the predictive model;

applying the original query and the revised queries to the predictive model to obtain prediction measures for the revised queries, the prediction measures serving as confidence measures for ranking the revised queries; and

sorting the revised queries by the prediction measures.

16. A computer program product for improving the likelihood of success of suggested revised queries, the computer program product comprising:

a computer-readable medium; and

computer program code, coded on the medium, for;

maintaining log files of user clicks on revised queries associated with an original query;

generating, using the log files, a predictive model to estimate a likelihood of success of the revised queries with respect to the original queries; and

applying the original query and the revised queries to the predictive model to obtain scores for the revised queries.

17. A computer program product for improving the likelihood of success of suggested revised queries, the computer program product comprising:

a computer-readable medium; and

computer program code, coded on the medium, for;

maintaining log files of user clicks on a list of revised queries associated with an original query;

selecting features associated with the revised queries;

collecting click data from the log files;

formulating a rule using the features and the click data;

using the rule to generate a predictive model to estimate a likelihood of success of the revised-queries;

applying the original query and the revised queries to the predictive model to obtain prediction measures for the revised queries; and

sorting the revised queries by the prediction measures.

18. A computer program product for improving the likelihood of success of suggested revised queries, the computer program product comprising:

a computer-readable medium; and

computer program code, coded on the medium, for;

maintaining log files of user clicks on a list of revised queries associated with an original query, the log files including features associated with the original query and the revised queries;

selecting a subset of features associated with the revised queries;

collecting click data from the log files, the click data including click length data, wherein longer clicks indicate greater user satisfaction;

formulating a rule using the subset of features and the click data;

using the rule to generate a predictive model to estimate a likelihood of success of the revised queries, the predictive model including a series of rules using the subset of the features;

training the predictive model, comprising;

formulating additional rules using the click data; and

selectively adding the additional rules to the predictive model;

applying the original query and the revised queries to the predictive model to obtain prediction measures for the revised queries, the prediction measures serving as confidence measures for ranking the revised queries; and

sorting the revised queries by the prediction measures.

19. A system for improving the likelihood of success of suggested revised queries, the system comprising:

means for maintaining log files of user clicks on revised queries associated with an original query;

means for generating, using the log files, a predictive model to estimate a likelihood of success of the revised queries with respect to the original queries; and

means for applying the original query and the revised queries to the predictive model to obtain scores for the revised queries.

20. A system for improving the likelihood of success of suggested revised queries, the system comprising:

means for maintaining log files of user clicks on a list of revised queries associated with an original query;

means for selecting features associated with the revised queries;

means for collecting click data from the log files;

means for formulating a rule using the features and the click data;

means for using the rule to generate a predictive model to estimate a likelihood of success of the revised queries;

means for applying the original query and the revised queries to the predictive model to obtain prediction measures for the revised queries; and

means for sorting the revised queries by the prediction measures.

21. A system for improving the likelihood of success of suggested revised queries, the system comprising:

means for maintaining log files of user clicks on a list of revised queries associated with an original query, the log files including features associated with the original query and the revised queries;

means for selecting a subset of features associated with the revised queries;

means for collecting click data from the log files, the click data including click length data, wherein longer clicks indicate greater user satisfaction;

means for formulating a rule using the subset of features and the dick data;

means for using the rule to generate a predictive model to estimate a likelihood of success of the revised queries, the predictive model including a series of rules using the subset of the features;

means for training the predictive model, comprising;

means for formulating additional rules using the click data; and

means for selectively adding the additional rules to the predictive model;

means for applying the original query and the revised queries to the predictive model to obtain prediction measures for the revised queries, the prediction measures serving as confidence measures for ranking the revised queries; and

means for sorting the revised queries by the prediction measures.