Adapting a search classifier based on user queries

• US 20070276818A1
• Filed: 07/30/2007
• Published: 11/29/2007
• Est. Priority Date: 12/05/2002
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Cook, Daniel, Goldberg, Matthew, Oftedal, Chad, Seiber, Scott
• Application Number:
  US11/830,375
• Publication Number:
  US 20070276818A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/355 Class or cluster creation or modification
• CPC Class Codes: Y10S 707/99934 Query formulation, input preparation, or translation
• CPC Class Codes: Y10S 707/99942 Manipulating data structure, e.g. compression, compaction, compilation
• CPC Class Codes: Y10S 707/99943 Generating database or data structure, e.g. via user interface

Abstract

Multiple different user queries are applied to an automated classifier to identify multiple tasks. For each query, a task is provided to a user. A task selected by the user is logged and a mapping between each query and each selected task is generated. Fewer than all of the mappings are used to train a new classifier, wherein selecting fewer than all of the mappings to train the new classifier comprises selecting mappings based on when the mappings were generated. The new classifier is stored on a computer-readable storage medium.

Claims

1. A computer-readable storage medium having computer-executable instructions for performing steps comprising:

applying multiple different user queries to an automated classifier to identify multiple tasks, each user query comprising at least one word;

for each user query;

providing a task identified for the user query to a user;

logging a task selected by the user;

generating a mapping between each query and each selected task;

selecting fewer than all of the mappings to train a new classifier, wherein selecting fewer than all of the mappings to train the new classifier comprises selecting mappings based on when the mappings were generated; and

storing the new classifier on a computer-readable storage medium, the new classifier for identifying at least one task from a user query.

2. The computer-readable storage medium of claim 1 further comprising using a first set of mappings to train a first new classifier and a second set of mappings, different from the first set of mappings, to train a second new classifier.

3. The computer-readable storage medium of claim 2 further comprising testing the first new classifier and the second new classifier to determine which performs better.

4. The computer-readable storage medium of claim 1 wherein training a classifier comprises setting different training parameters for different tasks.

5. The computer-readable storage medium of claim 4 wherein setting a training parameter for a first task comprises selecting a first percentage of mappings produced for the first task, and setting a training parameter for a second task comprises selecting a second percentage of mappings produced for the second task, the first percentage being different from the second percentage.

6. A method comprising:

applying multiple different user queries to an automated classifier to identify multiple tasks;

for each query, providing a task identified for the query to a user;

for at least two queries, logging a task selected by the user;

generating a mapping between each query for which a task was selected and each selected task;

selecting fewer than all of the mappings to train a new classifier by selecting mappings based on when the mappings were generated; and

storing the new classifier on a computer-readable storage medium, the new classifier for identifying at least one task from a user query.

7. The method of claim 6 further comprising using a first set of mappings to train a first new classifier and a second set of mappings, different from the first set of mappings, to train a second new classifier.

8. The method of claim 7 further comprising testing the first new classifier and the second new classifier to determine which performs better.

9. The method of claim 6 wherein training a classifier comprises setting different training parameters for different tasks.

10. The method of claim 9 wherein setting a training parameter for a first task comprises selecting a first percentage of mappings produced for the first task, and setting a training parameter for a second task comprises selecting a second percentage of mappings produced for the second task, the first percentage being different from the second percentage.

11. A method comprising:

receiving input designating a first percentage of mappings between a first task and a first set of queries that is to be used to train a classifier, the first percentage less than one-hundred percent;

receiving input designating a second percentage of mappings between a second task and a second set of queries that is to be used to train the classifier, the second percentage less than one-hundred percent;

retrieving the first percentage of mappings between the first task and the first set of queries by selecting the latest formed mappings between the first task and the first set of queries up to the first percentage;

retrieving the second percentage of mappings between the second task and the second set of queries by selecting the latest formed mappings between the second task and the second set of queries up to the second percentage;

using the retrieved mappings to train a classifier for classifying a query into at least one task; and

storing the classifier on a computer-readable storage medium.

12. The method of claim 11 further comprising forming mappings between the first task and the first set of queries through steps comprising:

receiving a query from a user;

identifying a task for the query and displaying the task to the user;

logging a task selected by the user and the query; and

using the logged task and the query to form the mappings.