OPTIMIZING AN INDEX OF WEB DOCUMENTS

• US 20120233096A1
• Filed: 03/07/2011
• Published: 09/13/2012
• Est. Priority Date: 03/07/2011
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: KUMAR, GIRISH, GUPTA, ATUL KUMAR, TIMASHEVA, ANNA V., WANG, YUAN, PANUGANTI, RAJKIRAN, GHOSH, GARGI, QIN, CHAOPING, GANJISAFFAR, YASSER, ZHOU, HONGYAN
• Application Number:
  US13/042,016
• Publication Number:
  US 20120233096A1
• Time in Patent Office: Days
• US Class Current: 706/12
• CPC Class Codes: G06F 16/31 Indexing; Data structures therefor; Storage structures
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques

Abstract

Historical usage data related to user queries and training properties for a plurality of web pages is received and utilized to train a mathematical model to predict the likelihood of retrieval of a web page during a web search. Properties are extracted from the plurality of web pages in the index and the mathematical model is applied to the properties for each web page to calculate a sortrank value. The index is reordered based on the sortrank value such that the web pages most likely to be retrieved by a user submitting a search query appear first in the index. After a search query is received from a user the index is traversed in an order determined by the sortrank value. Responsive web pages are presented to the user in an order determined by a search engine ranking algorithm.

Claims

1. One or more computer storage media (the “

media”

) storing computer-useable instructions that, when used by one or more computing devices, cause the one or more computing devices to perform a method for predicting the likelihood of retrieval of web documents during a web search, the method comprising;

receiving historical usage data related to user queries and training properties of a plurality of web pages in an index;

training a mathematical model to predict a likelihood of retrieval for the plurality of web pages based on the historical usage data and the training properties;

extracting properties from the plurality of web pages in the index;

applying the mathematical model to the properties;

calculating a sortrank value for each web page based on the mathematical model and the properties;

reordering the index based on the sortrank value for each web page;

2. The media of claim 1 further comprising:

receiving a query from a user;

traversing the index in an order determined by the sortrank value; and

presenting responsive web pages in an order determined by a search engine ranking algorithm.

3. The media of claim 1, wherein the historical usage data comprises data about previous user queries, click analytics, behavioral targeting, geolocation, page tagging, logfile analysis, or a combination thereof.

4. The media of claim 1, wherein the properties are query independent.

5. The media of claim 1, wherein the properties comprise a static rank, a domain rank, a tool bar domain hit count, a tool bar domain user count, a junk page measure, a spam page measure, an anchor most frequent count, a body most frequent count, an anchor unique phrase count, an anchor total phrase count, an anchor unique term count, a body term count, a top level domain rating, a words in domain count, a words in path count, a words in title count, a total anchor count, a number of entries in the Open Directory Project count, a tool bar uniform resource locator hit count, a tool bar uniform resource locator user count, or a combination thereof.

6. The media of claim 1, wherein the mathematical model utilizes a weight factor assigned to each property to signify an importance of the property when calculating the sortrank value.

7. A computer system for predicting the likelihood of retrieval of web documents during a web search, the computer system comprising a processor coupled to a computer-storage medium, the computer-storage medium having stored thereon a plurality of computer software components executable by the processor, the computer software components comprising:

an extraction component for extracting properties from a plurality of web pages in an index;

a ranking component for determining a sortrank value for each web page based on the properties; and

an indexing component for reordering the index based on the sortrank value;

8. The system of claim 7, further comprising:

a query component for receiving a query from a user;

traversing the index in an order determined by the sortrank value; and

a results component for identifying responsive web pages to the query in an order determined by a search engine ranking algorithm.

9. The computer system of claim 7, wherein the properties comprise a static rank, a domain rank, a tool bar domain hit count, a tool bar domain user count, a junk page measure, a spam page measure, an anchor most frequent count, a body most frequent count, an anchor unique phrase count, an anchor total phrase count, an anchor unique term count, a body term count, a top level domain rating, a words in domain count, a words in path count, a words in title count, a total anchor count, a number of entries in the Open Directory Project count, a tool bar uniform resource locator hit count, a tool bar uniform resource locator user count, or a combination thereof.

10. The computer system of claim 7, further comprising a training component for training the ranking component.

11. The computer system of claim 10, further comprising a historical component for receiving historical usage data.

12. The computer system of claim 11, wherein the training component utilizes the historical usage data and training properties associated with a sample of web pages in the index for training the ranking component.

13. The computer system of claim 11, wherein the historical usage data comprises data about previous user queries, click analytics, behavioral targeting, geolocation, page tagging, logfile analysis, or a combination thereof.

14. The computer system of claim 7, further comprising a weighting component for assigning weight factors to the properties.

15. A computerized method for predicting the likelihood of retrieval of web documents, the method comprising:

receiving historical usage data based on a frequency of web page retrieval for a sample query set;

training a mathematical model with the historical usage data and training properties of web pages to predict a likelihood of retrieval;

extracting one or more query independent properties from a plurality of web pages in an index;

determining, by the mathematical model, a sortrank value for each web page;

assigning the sortrank value to each web page based on the one or more query independent properties; and

sorting the plurality of web pages in the index based on the sortrank value.

16. The method of claim 15, wherein the historical usage data comprises data about previous user queries, click analytics, behavioral targeting, geolocation, page tagging, logfile analysis, or a combination thereof.

17. The method of claim 15, wherein the properties comprise a static rank, a domain rank, a tool bar domain hit count, a tool bar domain user count, a junk page measure, a spam page measure, an anchor most frequent count, a body most frequent count, an anchor unique phrase count, an anchor total phrase count, an anchor unique term count, a body term count, a top level domain rating, a words in domain count, a words in path count, a words in title count, a total anchor count, a number of entries in the Open Directory Project count, a tool bar uniform resource locator hit count, a tool bar uniform resource locator user count, or any combination thereof.

18. The method of claim 17, wherein the properties are assigned a weight factor.

19. The method of claim 15, further comprising receiving a query and retrieving responsive web pages.

20. The method of claim 19, further comprising traversing the index in an order determined by the sortrank value and displaying the responsive web pages in an order determined by a search engine ranking algorithm.