System and method for ranking search results using file types

• US 20060200460A1
• Filed: 03/03/2005
• Published: 09/07/2006
• Est. Priority Date: 03/03/2005
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Robertson, Stephen E., Meyerzon, Dmitriy, Taylor, Michael J., Zaragoza, Hugo
• Application Number:
  US11/073,381
• Publication Number:
  US 20060200460A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/335 Filtering based on additional data, e.g. user or group profiles filtering in web context G06F16/9535, G06F16/9536
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation

Abstract

Search results of a search query on a network are ranked according to an additional ranking function for the prior probability of relevance of a document based on document property. The document property may be the document'"'"'s file type, the file size, the document language, or another query-independent property of the document. The query-independent values for each document property may be weighted according to relevance measurements of the document based on the document property. As more relevance measurements of the documents may be obtained, the query-independent values for each document property may be updated to reflect the new measurements.

Claims

1. A computer-implemented method for ranking search results, comprising:

detecting a first property associated with each document in a collection of documents;

wherein the first property is a file type associated with the document;

storing the first property of each document in an index;

estimating a query-independent value corresponding to the first property for each document, wherein the query-independent value corresponds to a measure of the relevance of each document based on the first property; and

ranking each document that is responsive to a search query to obtain the search results, wherein each document is ranked based on the query-independent value.

2. The computer-implemented method of claim 1, further comprising detecting a second property associated with each document, wherein the second property corresponds to at least one of a file size range and a document language.

3. The computer-implemented method of claim 1, further comprising combining the query-independent value associated with the first property with another query-independent value associated with a second property to obtain a combined query-independent value for each document.

4. The computer-implemented method of claim 3, further comprising storing the combined query-independent value in the index for each of the documents while awaiting a search query.

5. The computer-implemented method of claim 3, further comprising combining at least one query-dependent value with the combined query-independent value to produce a score for each document, wherein each document is ranked according to the score for each document.

6. The computer-implemented method of claim 1, wherein the query-independent value for each file type is generated from relevance judgments made regarding the file types, such that each file type is weighted against the other file types.

7. The computer-implemented method of claim 1, further comprising adjusting the query-independent value for each document, as additional search queries are performed and a relevance measure of the search results based on the file types is made.

8. The computer-implemented method of claim 1, wherein ranking each document based on the query-independent value further comprises using a component corresponding to the first property in a scoring function for determining a relevance score for each of the documents.

9. The computer-implemented method of claim 1, wherein the first property for a document is stored within a pseudo-key that associates the first property with the document in the index.

10. The computer-implemented method of claim 1, further comprising ranking the documents according to a scoring function (score) that is determined according to at least:

the first property (W(t)).

11. The computer-implemented method of claim 10, wherein the scoring function (score) is further determined according to:

a computed click distance (CD), a weight of a query-independent component (w_cd), a weight of the click distance (b_cd), a weight of a URL depth (b_ud), the URL depth (UD), and a click distance saturation constant (K_cd).

12. The computer-implemented method of claim 11, wherein the scoring function (score) is further determined according to:

a weighted term frequency (wtf), a weighted document length (wdl), an average weighted document length (avwdl), a number of documents on the network (N);

a number of documents containing a query term (n), and other constants (k₁, b).

13. The computer-implemented method of claim 12, wherein the scoring function (score) is given by:

$\begin{array}{c}\mathrm{score}=<br><br>\sum <br><br>\frac{\mathrm{wtf}<br><br>\left(k_1+1\right)}{k_1<br><br>\left(\left(1-b\right)+b<br><br>\frac{w<br><br>d<br><br>l}{\mathrm{avw}<br><br>d<br><br>l}\right)+\mathrm{wtf}}\times <br><br>\log <br><br>\text{\hspace{1em}<br><br>}<br><br>\left(\frac{N}{n}\right)+\\ <br><br>w_{c<br><br>\text{\hspace{1em}<br><br>}<br><br>d}<br><br>\frac{k_{c<br><br>\text{\hspace{1em}<br><br>}<br><br>d}}{k_{c<br><br>\text{\hspace{1em}<br><br>}<br><br>d}+\frac{b_{c<br><br>\text{\hspace{1em}<br><br>}<br><br>d}<br><br>\mathrm{CD}+b_{\mathrm{ud}}<br><br>\mathrm{UD}}{b_{c<br><br>\text{\hspace{1em}<br><br>}<br><br>d}+b_{\mathrm{ud}}}}+W<br><br>\left(t\right)\end{array}$

14. A system for ranking search results, comprising:

a search engine include on a computing device, the search engine configured to execute computer-executable instructions, the computer-executable instructions comprising;

detecting a first property associated with each document in a collection of documents, wherein the first property corresponds to at least one of a file type, a file size range, and a document language;

storing the first property of each document in an index;

estimating a query-independent value corresponding to the first property for each document, wherein the query-independent value corresponds to a measure of the relevance of each document based on the first property; and

ranking each document that is responsive to a search query to obtain the search results, wherein each document is ranked based on the query-independent value.

15. The system of claim 14, wherein the computer-executable instructions further comprise ranking the documents according to a scoring function (score) that is determined according to at least:

the first property (W(t)).

16. The system of claim 15, wherein the scoring function (score) is further determined according to:

a computed click distance (CD), a weight of a query-independent component (w_cd), a weight of the click distance (b_cd), a weight of a URL depth (b_ud), the URL depth (UD), a click distance saturation constant (K_cd), a weighted term frequency (wtf), a weighted document length (wdl), an average weighted document length (avwdl), a number of documents on the network (N);

a number of documents containing a query term (n), and other constants (k₁, b), and the scoring function (score) is given by;

$\begin{array}{c}\mathrm{score}=<br><br>\sum <br><br>\frac{\mathrm{wtf}<br><br>\left(k_1<br><br>\text{\hspace{1em}<br><br>}+\text{\hspace{1em}<br><br>}<br><br>1\right)}{k_1<br><br>\left(\left(1<br><br>\text{\hspace{1em}<br><br>}-\text{\hspace{1em}<br><br>}<br><br>b\right)<br><br>\text{\hspace{1em}<br><br>}+\text{\hspace{1em}<br><br>}<br><br>b<br><br>\text{\hspace{1em}<br><br>}<br><br>\frac{\text{\hspace{1em}<br><br>}<br><br>w<br><br>\text{\hspace{1em}<br><br>}<br><br>d<br><br>l}{\mathrm{avw}<br><br>\text{\hspace{1em}<br><br>}<br><br>d<br><br>l}\right)<br><br>\text{\hspace{1em}<br><br>}+\text{\hspace{1em}<br><br>}<br><br>\mathrm{wtf}}\times <br><br>\log <br><br>\text{\hspace{1em}<br><br>}<br><br>\left(\frac{N}{n}\right)+<br><br>\text{\hspace{1em}<br><br>}\\ <br><br>w_{c<br><br>\text{\hspace{1em}<br><br>}<br><br>d}<br><br>\frac{k_{c<br><br>\text{\hspace{1em}<br><br>}<br><br>d}}{k_{c<br><br>\text{\hspace{1em}<br><br>}<br><br>d}+\frac{b_{c<br><br>\text{\hspace{1em}<br><br>}<br><br>d}<br><br>\mathrm{CD}+b_{\mathrm{ud}}<br><br>\mathrm{UD}}{b_{c<br><br>\text{\hspace{1em}<br><br>}<br><br>d}+b_{\mathrm{ud}}}}+W<br><br>\left(t\right)\end{array}$

17. A computer-readable medium that includes computer-executable instructions for ranking search results, the computer-executable instructions comprising:

storing a first property of each document in an index, wherein the first property corresponds to a file type;

estimating a query-independent value corresponding to the first property for each document, wherein the query-independent value corresponds to a measure of the relevance of each document based on the first property;

combining the query-independent value associated with the first property with another query-independent value associated with a second property to obtain a combined query-independent value for each document;

storing the combined query-independent value in the index for each of the documents while awaiting a search query; and

ranking each document that is responsive to a search query to obtain the search results, wherein each document is ranked based on the combined query-independent value.

18. The computer-readable medium of claim 17, further comprising combining at least one query-dependent value with the combined query-independent value to produce a score for each document, wherein each document is ranked according to the score for each document.

19. The computer-readable medium of claim 17, wherein the query-independent value for each file type is generated from relevance judgments made regarding the file types, such that each file type is weighted against the other file types.

20. The computer-readable medium of claim 17, further comprising adjusting the query-independent value for each document, as additional search queries are performed and a relevance measure of the search results based on the file types is made.