PROVIDING SEARCH RESULTS IN RESPONSE TO A SEARCH QUERY

• US 20110270828A1
• Filed: 04/29/2010
• Published: 11/03/2011
• Est. Priority Date: 04/29/2010
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Varma, Manik, Jain, Vidit
• Application Number:
  US12/769,672
• Publication Number:
  US 20110270828A1
• Time in Patent Office: Days
• US Class Current: 707/728
• CPC Class Codes: G06F 16/53 Querying
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques

Abstract

A method for providing search results in response to a search query is provided. The method includes receiving the search query from a user and generating a plurality of results in response to the search query. The plurality of results may be ranked according to an original relevancy score. The method further includes generating a click relevancy score for each of the plurality of results and re-ranking the plurality of results according to the click relevancy score.

Claims

1. A method for providing search results in response to a search query, comprising:

receiving the search query from a user;

generating a plurality of results in response to the search query, wherein the plurality of results is ranked according to an original relevancy score;

generating a click relevancy score for each result, wherein the click relevancy score is based on a number of clicks each result is expected to receive;

re-ranking the plurality of results according to the click relevancy score; and

presenting the plurality of results according to the click relevancy score.

2. The method of claim 1, wherein the plurality of results is presented in order of decreasing relevancy.

3. The method of claim 1, further comprising determining the original relevancy score via a baseline ranker.

4. The method of claim 1, wherein the search query is an image search query.

5. The method of claim 1, wherein the plurality of results comprises one or more clicked results that have received clicks in the past in response to the search query

6. The method of claim 5, wherein generating the click relevancy score for each result comprises estimating a predicted number of clicks for each result.

7. The method of claim 6, wherein estimating the predicted number of clicks comprises selecting one or more features that are associated with each result according to a dimensionality reduction algorithm.

8. The method of claim 7, wherein the dimensionality reduction algorithm is Principal Component Analysis.

9. The method of claim 7, wherein the features comprise textual features, visual features or combinations thereof.

10. The method of claim 9, wherein estimating the predicted number of clicks further comprises performing a regression analysis to compare a similarity between the features of each result with the features of the one or more clicked results.

11. The method of claim 10, wherein performing the regression analysis comprises performing a Gaussian Process Regression.

12. The method of claim 9 wherein the click relevancy score for each result is determined by the equation s_R(x)=a₁ s_O(x)+a₂ y_Text(x)+a₃ y_Visual(x), wherein x represents each result, s_R represents the click relevancy score, s_O represents the original relevancy score, y_Text represents the predicted number of clicks using textual features of each result, y_Visual represents the predicted number of clicks using visual features of each result, and a₁, a₂ and a₃ represent global weighting constants.

13. The method of claim 9, wherein the predicted number of clicks for each result is estimated using the equation y(f)=k(f, f_Train) [k(f_Train, f_Train)+σ

²l]<sup>−

1</sup> y_Train, wherein y represents the predicted number of clicks, y_Train is a vector representing a number of actual clicks recorded for each clicked result, f is a vector representing the one or more features that are associated with each result, f_Train is a vector representing one or more features that are associated with each clicked result, k represents a kernel function such that k(f_i, f_j)=exp(−

γ

|f_i−

f_j|²), and σ

is a noise parameter.

14. A computer-readable storage medium having stored thereon computer-executable instructions which, when executed by a computer, cause the computer to:

receive a search query from a user;

generate a plurality of results in response to the search query, each result having a plurality of features;

rank the plurality of results according to an original relevancy score determined by a baseline ranker;

generate a click relevancy score for each result, wherein the click relevancy score is based on a number of clicks each result is expected to receive;

re-rank the plurality of results according to the click relevancy score; and

present the plurality of results according to the click relevancy score.

15. The computer-readable storage medium of claim 14, wherein the plurality of results comprises one or more clicked results that have received clicks in the past in response to the search query.

16. The computer-readable storage medium of claim 15, wherein the computer-executable instructions to generate a click relevancy score for each result comprises computer-executable instructions which, when executed by a computer, cause the computer to:

select one or more features that are associated with each result according to a dimensionality reduction algorithm; and

perform a regression analysis comparing the similarity between the features of each result with the features of the clicked results.

17. A computer system, comprising:

at least one processor; and

a memory comprising program instructions that when executed by the at least one processor, cause the at least one processor to;

receive an image search query from a user;

generate a plurality of image results in response to the image search query, the each image result having one or more features;

rank the plurality of image results according to an original relevancy score determined by a baseline ranker;

perform a regression analysis to compare a similarity between the features of each image result with the features of one or more clicked image results to estimate a predicted number of clicks for each image result;

combine the predicted number of clicks with the original relevancy score to generate a click relevancy score for each image result;

re-rank the plurality of image results according to the click relevancy score; and

present the plurality of results according to the click relevancy score.

18. The computer system of claim 17, wherein the regression analysis is Gaussian Process Regression.

19. The computer system of claim 17, wherein the memory further comprises program instructions configured to select the features of each image result according to a dimensionality reduction algorithm.

20. The computer system of claim 17, wherein the features comprise textual features, visual features or combinations thereof.