Systems and methods for ranking implicit search results

• US 7,693,825 B2
• Filed: 03/31/2004
• Issued: 04/06/2010
• Est. Priority Date: 03/31/2004
• Status: Active Grant

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: Wang, Niniane, Lawrence, Stephen R.
• Primary Examiner(s): Wong; Don
• Assistant Examiner(s): DANG, THANH HA T
• Application Number:
  US10/813,875
• Publication Number:
  US 20070276829A1
• Time in Patent Office: 2,197 Days
• Field of Search: 707/100, 707/3, 707/7, 707/5
• US Class Current: 707/723
• CPC Class Codes: G06F 16/3334 Selection or weighting of terms from queries, including natural language queries
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques

Abstract

Systems and methods for ranking implicit search queries are described. In one embodiment a method comprising receiving an event, the event comprising user interaction with an article on a client device, wherein the article is capable of being associated with at least one of a plurality of client applications, extracting at least one keyword from the event, generating a query based at least in part on the at least one keyword, performing a search based at least in part on the query to determine a result set, wherein the result set comprises one or more article identifiers associated with articles comprising the at least one keyword, and determining a ranking for each of the one or more article identifiers comprising the result set is described.

Claims

1. A method of ranking article identifiers of a result set from an implicit query implied from a user'"'"'s current context, the method comprising:

receiving an event concerning the user'"'"'s current context, wherein the event comprises a user interaction with an article having content stored on a local client device, wherein the article is associated with at least one of a plurality of client applications;

analyzing the content of the article associated with the event concerning the user'"'"'s current context to extract at least one keyword and to identify one or more characteristics of the content of the article, a characteristic comprising highlighting of the content of the article;

generating the implicit query based at least in part on the at least one keyword;

performing a search based at least in part on the implicit query to determine the result set, wherein the result set comprises one or more article identifiers associated with articles relevant to the implicit query; and

ranking the article identifiers based at least in part on the one or more characteristics of the content of the article associated with the event concerning the user'"'"'s current context, wherein the one or more characteristics comprise the identified highlighting of the content of the article associated with the event.

2. The method of claim 1, wherein ranking the article identifiers is based at least in part on a preference of a current user.

3. The method of claim 2, wherein the preference of the current user is based at least in part on click-through data associated with the article identifiers.

4. The method of claim 2, wherein the preference of the current user is based at least in part on file type associated with the article identifiers.

5. The method of claim 1, wherein the one or more characteristics comprise bolding of content within the article.

6. The method of claim 1, wherein ranking the article identifiers is based at least in part on a term frequency and a document frequency.

7. The method of claim 6, wherein ranking the article identifiers comprises determining a rank that is proportional to the log of a sum of a first constant plus the term frequency and inversely proportional to the log of a sum of a second constant plus the document frequency.

8. The method of claim 1, wherein ranking the article identifiers comprises determining a rank that is proportional to the log of a sum of a constant plus a term frequency and inversely proportional to an output of a mapping function that maps ranges of document frequency into constants.

9. The method of claim 1, wherein the one or more characteristics comprise number data associated with the keyword within the article.

10. The method of claim 9, wherein the number data comprises a number of letters in the keyword.

11. The method of claim 9, wherein the number data comprises whether the keyword comprises numbers.

12. The method of claim 1, wherein the one or more characteristics comprise capitalization data associated with content within the article.

13. The method of claim 1, wherein ranking the article identifiers is based at least in part on a number of sources from which the keyword was located.

14. The method of claim 1, wherein ranking the article identifiers is based at least in part on a number of result sets in which the result set appears.

15. The method of claim 1, further comprising:

analyzing the content of the article associated with the event concerning the user'"'"'s current context to extract a plurality of keywords; and

determining keyword ranking scores for the plurality of keywords.

16. The method of claim 15, wherein ranking the article identifiers is based at least in part on the keyword ranking scores.

17. The method of claim 16, wherein ranking the article identifiers comprises assigning a higher ranking to article identifiers associated with articles containing higher ranked keywords.

18. The method of claim 1, wherein analyzing the content of the article associated with the event concerning the user'"'"'s current context to extract at least one keyword comprises extracting a keyword from at least one of recently typed words, an entire document, a selected portion of a document, or words surrounding a cursor.

19. The method of claim 1, wherein analyzing the content of the article associated with the event concerning the user'"'"'s current context to extract at least one keyword from an event comprises determining proper names.

20. The method of claim 19, wherein determining proper names comprises crawling at least one article.

21. The method of claim 1, further comprising:

filtering the result set based on a threshold; and

outputting the article identifiers associated with the filtered result set.

22. The method of claim 21, wherein the threshold comprises a number of keywords.

23. The method of claim 21, wherein the threshold comprises a minimum weighting score based at least in part on one or more of a number of keywords multiplier, a source multiplier, and a time multiplier.

24. The method of claim 1, wherein the article is a document on the client device, and wherein the event comprises an addition of words to the document.

25. The method of claim 1, wherein the article is a document on the client device, and wherein the event comprises a placement of a cursor near words in the document.

26. The method of claim 1, wherein the article is associated with one client application selected from a group consisting of a word processing program, a spreadsheet program, a presentation program, an e-mail program, an instant messenger program, and a database program.

27. The method of claim 1, wherein the one or more characteristics comprise italicizing of content within the article associated with the event.

28. The method of claim 1, wherein the one or more characteristics comprise font color of content within the article associated with the event.

29. The method of claim 1, wherein the one or more characteristics comprise heading data of content within the article associated with the event.

30. The method of claim 1, wherein the at least one keyword is extracted from recently typed words within the article associated with the event.

31. The method of claim 1, wherein the at least one keyword is extracted from a user selected portion within the article associated with the event.

32. The method of claim 1, wherein the at least one keyword is extracted from words surrounding a cursor within the article associated with the event.

33. A computer-readable storage medium containing program code for ranking article identifiers of a result set from an implicit query implied from a user'"'"'s current context, the program code comprising:

program code for receiving an event concerning the user'"'"'s current context, wherein the event comprises a user interaction with an article having content stored on a local client device, wherein the article is associated with at least one of a plurality of client applications;

program code for analyzing the content of the article associated with the event concerning the user'"'"'s current context to extract at least one keyword and to identify one or more characteristics of the content of the article, a characteristic comprising highlighting of the content of the article;

program code for generating the implicit query based at least in part on the at least one keyword;

program code for performing a search based at least in part on the implicit query to determine the result set, wherein the result set comprises one or more article identifiers associated with articles relevant to the implicit query; and

program code for ranking the article identifiers based at least in part on the one or more characteristics of the content of the article associated with the event concerning the user'"'"'s current context, wherein the one or more characteristics comprise the identified highlighting of the content of the article associated with the event.

34. The computer-readable storage medium of claim 33, wherein ranking the article identifiers is based at least in part on a preference of a current user.

35. The computer-readable storage medium of claim 34, wherein the preference of the current user is based at least in part on click-through data associated with the article identifiers.

36. The computer-readable storage medium of claim 34, wherein the preference of the current user is based at least in part on file type associated with the article identifiers.

37. The computer-readable storage medium of claim 33, wherein the one or more characteristics comprise bolding of content of the article.

38. The computer-readable storage medium of claim 33, wherein ranking the article identifiers is based at least in part on a term frequency and a document frequency.

39. The computer-readable storage medium of claim 38, wherein ranking the article identifiers comprises determining a rank that is proportional to the log of the sum of a first constant plus the term frequency and inversely proportional to the log of the sum of a second constant plus the document frequency.

40. The computer-readable storage medium of claim 33, wherein ranking the article identifiers comprises determining a rank that is proportional to the log of the sum of a constant plus a term frequency and inversely proportional to the output of a mapping function that maps ranges of document frequency into constants.

41. The computer-readable storage medium of claim 33, wherein the one or more characteristics comprise number data associated with the keyword within the article.

42. The computer-readable storage medium of claim 41, wherein the number data comprises a number of letters in the keyword.

43. The computer-readable storage medium of claim 41, wherein the number data comprises whether the keyword comprises numbers.

44. The computer-readable storage medium of claim 33, wherein the one or more characteristics comprise capitalization data associated with content within the article.

45. The computer-readable storage medium of claim 33, wherein ranking the article identifiers is based at least in part on a number of sources from which the keyword was located.

46. The computer-readable storage medium of claim 33, wherein ranking the article identifiers is based at least in part on a number of result sets in which the result set appears.

47. The computer-readable storage medium of claim 33, further comprising program code for:

analyzing the content of the article associated with the event concerning the user'"'"'s current context to extract a plurality of keywords; and

determining keyword ranking scores for the plurality of keywords.

48. The computer-readable storage medium of claim 47, wherein ranking the article identifiers is based at least in part on the keyword ranking scores.

49. The computer-readable storage medium of claim 48, wherein ranking the article identifiers comprises assigning a higher ranking to article identifiers associated with articles containing higher ranked keywords.

50. The computer-readable storage medium of claim 33, wherein analyzing the content of the article associated with the event concerning the user'"'"'s current context to extract at least one keyword comprises extracting a keyword from at least one of recently typed words, an entire document, a selected portion of a document, or words surrounding a cursor.

51. The computer-readable storage medium of claim 33, wherein analyzing the content of the article associated with the event concerning the user'"'"'s current context to extract at least one keyword comprises determining proper names.

52. The computer-readable storage medium of claim 51, wherein determining proper names comprises crawling at least one article.

53. The computer-readable storage medium of claim 33, the program code further comprising:

program code for filtering the result set based on a threshold; and

program code for outputting the article identifiers associated with the filtered result set.

54. The computer-readable storage medium of claim 53, wherein the threshold comprises a number of keywords.

55. The computer-readable storage medium of claim 53, wherein the threshold comprises a minimum weighting score based at least in part on one or more of a number of keywords multiplier, a source multiplier, and a time multiplier.

56. A method of ranking article identifiers of a result set from an implicit query implied from a user'"'"'s current context, the method comprising:

receiving a contextual event concerning the user'"'"'s current context, the contextual event comprising a user'"'"'s modification of a file having content stored on a local client device;

analyzing the content of the file stored on the local client device to extract at least one keyword and to identify one or more characteristics of the content of the file, a characteristic comprising highlighting of the content of the file;

generating the implicit query based at least in part on the at least one keyword extracted from the file;

performing a search based at least in part on the implicit query to determine the result set, wherein the result set comprises one or more article identifiers associated with articles comprising the at least one keyword;

determining a ranking score for the one or more article identifiers based at least in part on the one or more characteristics of the content of the file associated with the contextual event concerning the user'"'"'s current context, wherein the one or more characteristics comprise the identified highlighting of the content of the file associated with the contextual event; and

ranking the one or more article identifiers in the result set based on the ranking score.