System for ranking the relevance of information objects accessed by computer users

• US RE41,899 E1
• Filed: 03/12/2003
• Issued: 10/26/2010
• Est. Priority Date: 04/25/1994
• Status: Expired due to Term

• Current Assignee: Apple Inc.
• Original Assignee: Apple Inc.
• Inventors: Bornstein, Jeremy J., Ponceleón, Dulce B., Rose, Daniel E., Tiene, Kevin
• Primary Examiner(s): Sparks; Donald
• Assistant Examiner(s): Fernandez Rivas; Omar F
• Application Number:
  US10/388,362
• Time in Patent Office: 2,785 Days
• Field of Search: 706/45, 706/46, 706/14, 707/3
• US Class Current: 706/46
• 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

Abstract

Information presented to a user via an information access system is ranked according to a prediction of the likely degree of relevance to the user'"'"'s interests. A profile of interests is stored for each user having access to the system. Items of information to be presented to a user are ranked according to their likely degree of relevance to that user and displayed in order of ranking. The prediction of relevance is carried out by combining data pertaining to the content of each item of information with other data regarding correlations of interests between users. A value indicative of the content of a document can be added to another value which defines user correlation, to produce a ranking score for a document. Alternatively, multiple regression analysis or evolutionary programming can be carried out with respect to various factors pertaining to document content and user correlation, to generate a prediction of relevance. The user correlation data is obtained from feedback information provided by users when they retrieve items of information. Preferably, the user provides an indication of interest in each document which he or she retrieves from the system.

Claims

1. In a computerized information access system, a method for presenting items of information to users, comprising the steps of:

a) storing user profiles for users having access to the system, where each user profile is based, at least in part, on the attributes of information the user finds to be of interest;

b) determining an attribute-based relevance factor for an item of information which is indicative of the degree to which an attribute of that item of information matches the profile for a particular user;

c) determining a measure of correlation between the particular user'"'"'s interests and those of other users who have accessed said item of information;

d) combining said relevance factor and said degree of correlation to produce a ranking score for said item of information;

e) repeating steps b, c and d for each item of information to be presented to said particular user; and

f) displaying the items of information to the user in accordance with their ranking scores.

2. The method of claim 1, wherein said combining step comprises a regression analysis of attribute-based and correlation-based factors for each item of information.

3. The method of claim 1 wherein said combining step comprises forming a weighted sum of said relevance factor and said degree of correlation.

4. The method of claim 1, wherein said ranking score is also related to a date associated with each item of information.

5. The method of claim 1 wherein said step of determining said degree of correlation includes the steps of obtaining feedback information from users regarding each user'"'"'s interest in particular items of information when each such item is accessed by a user, and recording said feedback information.

6. The method of claim 5 further including the step of generating a correlation matrix which indicates the degree of correlation between respective users based upon commonly accessed items of information.

7. The method of claim 1 wherein said attribute is the contents of the item of information.

8. The method of claim 1 wherein said items of information are displayed in order of their relative rankings to thereby provide said indication.

9. The method of claim 1 wherein said relevance factor and said degree of correlation are combined by means of evolutionary programming techniques to generate a formula that is used to produce a ranking score for an item of information.

10. The method of claim 9 wherein said evolutionary programming technique comprises genetic programming.

11. The method of claim 9 wherein said evolutionary programming technique comprises genetic algorithms.

12. The method of claim 1 wherein said information access system is an electronic mail system, and said method is employed to filter messages provided to subscribers of said system.

13. The method of claim 1 wherein said information access system is an electronic bulletin board system, and said method is employed to rank items of information in a topic category selected by a user.

27. The method of claim 1 wherein the items of information are displayed with an indication of their ranking scores.

14. A computer-based information access system, comprising:

a first database containing items of information to be provided to users of said system;

means for enabling users to indicate their degree of interest in particular items of information stored in said first database;

means for determining the correlation between the indicated interests of respective users and for storing information related thereto; and

means for predicting a given user'"'"'s likely degree of interest in a particular item of information on the basis of said information relating to the determined correlation and at least one attribute of the item of information.

15. The information access system of claim 14 further including a user interface for displaying plural items of information with an indication of their relative predictions regarding likely degree of interest for a given user.

16. The information access system of claim 14 wherein said attribute is the contents of the item of information.

17. The information access system of claim 14 further including a second database containing at least one profile of interests for each of a number of users of said system, and wherein said prediction is based on a combination of (i) the relationship of said attribute to the profile for said given user and (ii) the correlation between indications provided by the given user and other users who have had access to said item of information.

18. The information access system of claim 17 wherein each user profile comprises a vector and said attribute defines a vector for the item of information, and wherein said relationship is determined in accordance with the similarities between the vector for the item of information and the user profile vector.

19. The information access system of claim 14 wherein said prediction is based on a regression analysis of data related to said attribute and stored correlation information pertaining to said given user.

20. The information access system of claim 14 wherein said prediction is determined by means of evolutionary programming techniques.

21. The information access system of claim 20 wherein the evolutionary programming techniques produce a formula which establishes a combination of attribute-based and correlation-based factors that determine said prediction.

22. The information access system of claim 20 wherein said evolutionary programming techniques comprise genetic programming.

23. The information access system of claim 20 wherein said evolutionary programming techniques comprise genetic algorithms.

24. The system of claim 14, wherein said information access system comprises an electronic mail system.

25. The system of claim 14, wherein said information access system comprises an electronic bulletin board system.

26. The system of claim 14, wherein said information access system comprises an electronic search and retrieval system.

28. A method for displaying items of information to users, comprising the steps of:

determining a relevance factor for an item of information, based upon an attribute of the item of information;

defining a relationship between the interests of a given user and those of other users;

determining a correlation factor for the item of information, based upon said defined relationship;

combining said relevance factor and said correlation factor to produce a ranking score for the item of information; and

displaying the item of information to the given user in accordance with its ranking score.

29. The method of claim 28 further including the steps of determining a ranking score for multiple items of information, and displaying the items of information in accordance with their ranking scores.

30. The method of claim 28 wherein the item of information is displayed with an indication of its ranking score.

96. The system of claim 29, wherein each user profile vector and each document vector comprises a plurality of weights, each weight associated with a term.

31. A method of presenting documents from a document collection to a user, the method comprising:

storing a user profile vector for the user, the user profile vector in a vector space derived from terms contained in the document collection and including a plurality of weights, each weight associated with a term in the document collection;

selecting a plurality of documents from the document collection, each document associated with a document vector in the term vector space;

for each selected document;

determining a relevance score, the relevance score based on a relationship between the user profile vector and the document vector associated with the selected document;

determining a correlation score between the user and other users corresponding to the selected document; and

combining the relevance score and the correlation score to determine a final ranking score for the selected document; and

presenting the selected documents to the user according to the final ranking scores.

32. The method of claim 31, wherein determining a correlation score comprises:

storing information relating to users'"'"' interest in the documents in the document collection;

storing information relating to the degree of correlation between the users'"'"' interest in documents;

generating the correlation score based upon the information relating to the users'"'"' interest and the information relating to the degree of correlation.

33. The method of claim 32, wherein:

the information relating to the users'"'"' interests in the documents is stored in a user interest matrix indicating the users'"'"' interests in particular documents;

the degree of correlation between the users'"'"' interest is stored in a correlation matrix indicating the degree of correlation between the users'"'"' interest in the documents; and

the correlation score is generated based upon the user interest matrix and the correlation matrix.

34. The method of claim 32, wherein:

storing information relating to the users'"'"' interest comprises generating a user interest matrix V where each entry V_kj is the weight indicating the feedback of user k on document j;

storing information relating to the degree of correlation comprises generating a correlation matrix R where each entry R_jk is a measure of the degree of correlation between users i and k; and

generating the correlation score comprises calculating a prediction score P_ij indicating a likelihood of user i'"'"'s interest in document j by carrying out an operation, $P_{\mathrm{ij}}=\underset{k\ne <br><br>i,j}{\sum <br><br>}<br><br>R_{\mathrm{ik}}<br><br>V_{\mathrm{kj}}.$

35. The method of claim 31, wherein the relationship between the user profile vector and the document vector is a cosine of an angle between the document vector and the user profile vector.

36. The method of claim 31, wherein the relationship between the user profile vector and the document vector is based on the similarity between the user profile vector and the document vector.

37. A computer program product for presenting documents from a document collection to a user, the computer program product stored on a computer readable medium and adapted to perform a method comprising:

storing a user profile vector for the user, the user profile vector in a vector space derived from terms contained in the document collection and including a plurality of weights, each weight associated with a term in the document collection;

selecting a plurality of documents from the document collection, each document associated with a document vector in the term vector space;

for each selected document;

determining a relevance score, the relevance score based on a relationship between the user profile vector and the document vector associated with the selected document;

determining a correlation score between the user and other users corresponding to the selected document; and

combining the relevance score and the correlation score to determine a final ranking score for the selected document; and

presenting the selected documents to the user according to the final ranking scores.

38. The computer program product of claim 37, wherein determining a correlation score comprises:

storing information relating to users'"'"' interest in the documents in the document collection;

storing information relating to the degree of correlation between the users'"'"' interest in documents;

generating the correlation score based upon the information relating to the users'"'"' interest and the information relating to the degree of correlation.

39. The computer program product of claim 38, wherein:

the information relating to the users'"'"' interests in the documents is stored in a user interest matrix indicating the users'"'"' interests in particular documents;

the degree of correlation between the users'"'"' interest is stored in a correlation matrix indicating the degree of correlation between the users'"'"' interest in the documents; and

the correlation score is generated based upon the user interest matrix and the correlation matrix.

40. The computer program product of claim 38, wherein:

storing information relating to the users'"'"' interest comprises generating a user interest matrix V where each entry V_kj is the weight indicating the feedback of user k on document j;

storing information relating to the degree of correlation comprises generating a correlation matrix R where each entry R_jk is a measure of the degree of correlation between users i and k; and

generating the correlation score comprises calculating a prediction score P_ij indicating a likelihood of user i'"'"'s interest in document j by carrying out an operation, $P_{\mathrm{ij}}=\underset{k\ne <br><br>i,j}{\sum <br><br>}<br><br>R_{\mathrm{ik}}<br><br>V_{\mathrm{kj}}.$

41. The computer program product of claim 37, wherein the relationship between the user profile vector and the document vector is a cosine of an angle between the document vector and the user profile vector.

42. The computer program product of claim 37, wherein the relationship between the user profile vector and the document vector is based on the similarity between the user profile vector and the document vector.

43. A system for presenting documents to a user, the documents each associated with a document vector in a vector space and stored in a document database coupled to the system, the system comprising:

a user database storing a user profile vector for the user, the user profile vector in the vector space derived from terms contained in the document database and including a plurality of weights, each weight associated with a term in the document collection; and

a server coupled to the user database and the document database for selecting documents from the document database, wherein the server;

determines, for each selected document, a relevance score, the relevance score based on a relationship between the user profile vector and the document vector associated with the selected document;

determines, for each selected document, a correlation score between the user and other users corresponding to the selected document;

combines, for each selected document, the relevance score and the correlation score to determine a final ranking score for the selected document; and

presents the selected documents to the user according to the final ranking scores.

44. The system of claim 43, wherein the server determines the correlation score by:

storing information relating to users'"'"' interest in the documents in the document collection;

storing information relating to the degree of correlation between the users'"'"' interest in documents;

generating the correlation score based upon the information relating to the users'"'"' interest and the information relating to the degree of correlation.

45. The system of claim 44, wherein:

the information relating to the users'"'"' interests in the documents is stored in a user interest matrix indicating the users'"'"' interests in particular documents;

the degree of correlation between the users'"'"' interest is stored in a correlation matrix indicating the degree of correlation between the users'"'"' interest in the documents; and

the server generates the correlation score based upon the user interest matrix and the correlation matrix.

46. The system of claim 44, wherein:

the information relating to the users'"'"' interest is stored in a user interest matrix V where each entry V_kj is the weight indicating the feedback of user k on document j;

the information relating to the degree of correlation is stored in a correlation matrix R where each entry R_jk is a measure of the degree of correlation between users i and k; and

the server generates the correlation score by calculating a prediction score P_ij indicating a likelihood of user i'"'"'s interest in document j by carrying out an operation, $P_{\mathrm{ij}}=\underset{k\ne <br><br>i,j}{\sum <br><br>}<br><br>R_{\mathrm{ik}}<br><br>V_{\mathrm{kj}}.$

47. The system of claim 43, wherein the relationship between the user profile vector and the document vector is a cosine of an angle between the document vector and the user profile vector.

48. The method of claim 43, wherein the relationship between the user profile vector and the document vector is based on the similarity between the user profile vector and the document vector.

49. A method of presenting information items from an information item collection to a user, the method comprising:

storing a user profile vector for the user, the user profile vector in a vector space derived from attributes in the information item collection and including a plurality of weights, each weight associated with an attribute in the information item collection;

selecting a plurality of information items from the information item collection, each information item associated with an information item vector in the attribute vector space;

for each selected information item;

determining a relevance score, the relevance score based on a relationship between the user profile vector and the information item vector associated with the selected information item;

determining a correlation score between the user and other users corresponding to the selected information item; and

combining the relevance score and the correlation score to determine a final ranking score for the selected information item; and

presenting the selected information items to the user according to the final ranking scores.

50. The method of claim 49, wherein determining a correlation score comprises:

storing information relating to users'"'"' interest in the information items in the information item collection;

storing information relating to the degree of correlation between the users'"'"' interest in information items;

generating the correlation score based upon the information relating to the users'"'"' interest and the information relating to the degree of correlation.

51. The method of claim 50, wherein:

the information relating to the users'"'"' interests in the information items is stored in a user interest matrix indicating the users'"'"' interests in particular information items;

the degree of correlation between the users'"'"' interest is stored in a correlation matrix indicating the degree of correlation between the users'"'"' interest in the information items; and

the correlation score is generated based upon the user interest matrix and the correlation matrix.

52. The method of claim 50, wherein:

storing information relating to the users'"'"' interest comprises generating a user interest matrix V where each entry V_kj is the weight indicating the feedback of user k on information item j;

storing information relating to the degree of correlation comprises generating a correlation matrix R where each entry R_ik is a measure of the degree of correlation between users i and k; and

generating the correlation score comprises calculating a prediction score P_ij indicating a likelihood of user i'"'"'s interest in information item j by carrying out an operation, $P_{\mathrm{ij}}=\underset{k\ne <br><br>i,j}{\sum <br><br>}<br><br>R_{\mathrm{ik}}<br><br>V_{\mathrm{kj}}.$

53. The method of claim 49, wherein the relationship between the user profile vector and the document vector is a cosine of an angle between the document vector and the user profile vector.

54. The method of claim 49, wherein the relationship between the user profile vector and the document vector is the distance between the user profile vector and the document vector.

55. A computer program product for presenting information items from an information item collection to a user, the computer program product stored on a computer readable medium and adapted to perform a method comprising:

storing a user profile vector for the user, the user profile vector in a vector space derived from attributes contained in the information item collection and including a plurality of weights, each weight associated with an attribute in the information item collection;

selecting a plurality of information items from the information item collection, each information item associated with an information item vector in the attribute vector space;

for each selected information item;

determining a relevance score, the relevance score based on a relationship between the user profile vector and the information item vector associated with the selected information item;

determining a correlation score between the user and other users corresponding to the selected information item; and

combining the relevance score and the correlation score to determine a final ranking score for the selected information item; and

presenting the selected information items to the user according to the final ranking scores.

56. The computer program product of claim 55, wherein determining a correlation score comprises:

storing information relating to users'"'"' interest in the information items in the information item collection;

storing information relating to the degree of correlation between the users'"'"' interest in information items;

generating the correlation score based upon the information relating to the users'"'"' interest and the information relating to the degree of correlation.

57. The computer program product of claim 56, wherein:

the information relating to the users'"'"' interests in the information items is stored in a user interest matrix indicating the users'"'"' interests in particular information items;

the degree of correlation between the users'"'"' interest is stored in a correlation matrix indicating the degree of correlation between the users'"'"' interest in the information items; and

the correlation score is generated based upon the user interest matrix and the correlation matrix.

58. The computer program product of claim 56, wherein:

storing information relating to the users'"'"' interest comprises generating a user interest matrix V where each entry V_kj is the weight indicating the feedback of user k on information item j;

storing information relating to the degree of correlation comprises generating a correlation matrix R where each entry R_jk is a measure of the degree of correlation between users i and k; and

generating the correlation score comprises calculating a prediction score P_ij indicating a likelihood of user i'"'"'s interest in information item j by carrying out an operation, $P_{\mathrm{ij}}=\underset{k\ne <br><br>i,j}{\sum <br><br>}<br><br>R_{\mathrm{ik}}<br><br>V_{\mathrm{kj}}.$

59. The computer program product of claim 55, wherein the relationship between the user profile vector and the document vector is a cosine of an angle between the document vector and the user profile vector.

60. The computer program product of claim 55, wherein the relationship between the user profile vector and the document vector is based on the similarity between the user profile vector and the document vector.

61. A system for presenting information items to a user, the information items each associated with an information item vector in the attribute vector space and stored in an information item database coupled to the system, the system comprising:

a user database storing a user profile vector for the user, the user profile vector in a vector space derived from attributes contained in the information item database and including a plurality of weights, each weight associated with an attribute in the information item collection; and

a server coupled to the user database and the information item database for selecting information items from the information item database, wherein the server;

determines, for each selected information item, a relevance score, the relevance score based on a relationship between the user profile vector and the information item vector associated with the selected information item;

determines, for each selected information item, a correlation score between the user and other users corresponding to the selected information item;

combines, for each selected information item, the relevance score and the correlation score to determine a final ranking score for the selected information item; and

presents the selected information items to the user according to the final ranking scores.

62. The system of claim 61, wherein the server determines the correlation score by:

storing information relating to users'"'"' interest in the information items in the information item collection;

storing information relating to the degree of correlation between the users'"'"' interest in information items;

generating the correlation score based upon the information relating to the users'"'"' interest and the information relating to the degree of correlation.

63. The system of claim 62, wherein:

the information relating to the users'"'"' interests in the information items is stored in a user interest matrix indicating the users'"'"' interests in particular information items;

the degree of correlation between the users'"'"' interest is stored in a correlation matrix indicating the degree of correlation between the users'"'"' interest in the information items; and

the server generates the correlation score based upon the user interest matrix and the correlation matrix.

64. The system of claim 62, wherein:

the information relating to the users'"'"' interest is stored in a user interest matrix V where each entry V_kj is the weight indicating the feedback of user k on information item j;

the information relating to the degree of correlation is stored in a correlation matrix R where each entry R_ik is a measure of the degree of correlation between users i and k; and

the server generates the correlation score by calculating a prediction score P_ij indicating a likelihood of user i'"'"'s interest in information item j by carrying out an operation, $P_{\mathrm{ij}}=\underset{k\ne <br><br>i,j}{\sum <br><br>}<br><br>R_{\mathrm{ik}}<br><br>V_{\mathrm{kj}}.$

65. The server of claim 61, wherein the relationship between the user profile vector and the document vector is a cosine of an angle between the document vector and the user profile vector.

66. The server of claim 61, wherein the relationship between the user profile vector and the document vector is based on the similarity between the user profile vector and the document vector.

67. A method of presenting documents from a document collection to a user, the method comprising:

storing a user profile for the user, the user profile including terms contained in the document collection and weights respectively associated with the terms;

selecting a plurality of documents from the document collection, each document associated with a document profile, the document profile including terms contained in its associated document;

for each selected document;

determining a relevance score, the relevance score based on a relationship between the user profile and the document profile associated with the selected document;

determining a correlation score between the user and other users corresponding to the selected document; and

combining the relevance score and the correlation score to determine a final ranking score for the selected document; and

presenting the selected documents to the user according to the final ranking scores.

68. The method of claim 67, wherein the final ranking score comprises a recommendation score.

69. The method of claim 68, wherein the recommendation score comprises a movie recommendation score.

70. A method comprising:

storing a user profile for a user, the user profile including terms contained in a document collection and weights respectively associated with the terms;

selecting a plurality of documents from the document collection, each document associated with a document profile, the document profile including terms contained in its associated document;

for each selected document;

determining a relevance score, the relevance score based on a relationship between the user profile and the document profile associated with the selected document;

determining a correlation score between the user and other users corresponding to the selected document; and

combining the relevance score and the correlation score to determine a final ranking score for the selected document; and

presenting one or more recommendations to the user based on the final ranking scores.

71. The method of claim 70, wherein the recommendations comprise movie recommendations.

72. A method of presenting documents received from a document collection to a user, the method comprising:

retrieving a user profile vector associated with the user, the user profile vector in a vector space derived from terms in the document collection;

receiving a plurality of documents from the document collection, each document having a document vector in the vector space;

for each received document;

determining a relevance score for the document by a vector operation comparing the user profile vector and the document vector; and

determining a correlation score between the user and other users corresponding to the document; and

ranking the received documents based on a combination of each received document'"'"'s relevance score and correlation score for presentation to the user.

73. The method of claim 72, wherein the vector space is defined by a set of terms selected from the terms in the document collection, each user profile vector and each document vector includes a plurality of vector components, each vector component corresponding to a weight of one of the terms.

74. The method of claim 72, wherein the vector operation is the determination of a cosine of an angle between the document vector and the user profile vector.

75. The method of claim 72, wherein the vector operation is a geometric operation determining a distance between the user profile vector and the document vector.

76. The method of claim 72, wherein each user profile vector and each document vector comprises a plurality of weights, each weight associated with a term.

77. The method of claim 72, wherein each user profile vector comprises a plurality of user profile vector weights derived from the user'"'"'s interest in documents and each document vector comprises a plurality of document vector weights indicating the frequency of occurrence of the terms associated with the document vector weights in the document.

78. The method of claim 72, further comprising

receiving a user rating of a document;

responsive to positive user rating, modifying the user profile vector of the user so that the user profile vector is more similar to the document vector of the user rated document; and

responsive to a negative user rating, modifying the user profile vector of the user so that the user profile vector is less similar to the document vector of the user rated document.

79. The method of claim 72, further comprising:

receiving a user rating of a document; and

modifying the user profile vector as a function of the user rating and the document vector of the user rated document.

80. The method of claim 72, further comprising:

receiving a user rating of a document indicating a user interest in the user rated document; and

modifying the user profile vector by determining which terms of the user rated document are significant and increasing the weights corresponding to the significant terms in the user profile vector.

81. The method of claim 72, wherein the document collection includes a first document database and a second document database separate from the first document database, and the user profile vector associated with the user comprises a first user profile vector and a second user profile vector, the first and second user profile vectors corresponding to the first and second document databases, respectively, the method further comprising:

updating the first user profile vector in response to a user rating of a document from the first document database; and

updating the second user profile vector in response to a user rating of a document from the second document database.

82. A computer program product for presenting documents received from a document collection to a user, the computer program product stored on a computer readable medium and configured to perform a method comprising:

retrieving a user profile vector associated with the user, the user profile vector in a vector space derived from terms in the document collection;

receiving a plurality of documents from the document collection, each document having a document vector in the vector space;

for each received document;

determining a relevance score for the document by a vector operation comparing the user profile vector and the document vector; and

determining a correlation score between the user and other users corresponding to the document; and

ranking the received documents based on a combination of each received document'"'"'s relevance score and correlation score for presentation to the user.

83. The computer program product of claim 82, wherein the vector space is defined by a set of terms selected from the terms in the document collection, each user profile vector and each document vector includes a plurality of vector components, each vector component corresponding to a weight of one of the terms.

84. The computer program product of claim 82, wherein the vector operation is the determination of a cosine of an angle between the document vector and the user profile vector.

85. The computer program product of claim 82, wherein the vector operation is a geometric operation determining a distance between the user profile vector and the document vector.

86. The computer program product of claim 82, wherein each user profile vector and each document vector comprises a plurality of weights, each weight associated with a term.

87. The computer program product of claim 82, wherein each user profile vector comprises a plurality of user profile vector weights derived from the user'"'"'s interest in documents and each document vector comprises a plurality of document vector weights indicating the frequency of occurrence of the terms associated with the document vector weights in the document.

88. The computer program product of claim 82, the method further comprising:

receiving a user rating of a document;

responsive to positive user rating, modifying the user profile vector of the user so that the user profile vector is more similar to the document vector of the user rated document; and

responsive to a negative user rating, modifying the user profile vector of the user so that the user profile vector is less similar to the document vector of the user rated document.

89. The computer program product of claim 82, the method further comprising:

receiving a user rating of a document; and

modifying the user profile vector as a function of the user rating and the document vector of the user rated document.

90. The computer program product of claim 82, the method further comprising:

receiving a user rating of a document indicating a user interest in the user rated document; and

modifying the user profile vector by determining which terms of the user rated document are significant and increasing the weights corresponding to the significant terms in the user profile vector.

91. The computer program product of claim 82, wherein the document collection includes a first document database and a second document database separate from the first document database, and the user profile vector associated with the user comprises a first user profile vector and a second user profile vector, the first and second user profile vectors corresponding to the first and second document databases, respectively, the method further comprising:

updating the first user profile vector in response to a user rating of a document from the first document database; and

updating the second user profile vector in response to a user rating of a document from the second document database.

92. A system for presenting documents to a user, the documents each having a document vector in a vector space and stored in a document database coupled to the system, the system comprising:

a user database storing a user profile vector associated with the user, the user profile vector in the vector space derived from terms in the document database;

a server coupled to the document database and the user database, the server receiving documents from the document database and determining a relevance score for each of the received documents by a vector operation comparing the user profile vector and the document vector and determining a correlation score for each of the received documents between the user and other users corresponding to the document and ranking the received documents based on a combination of each received document'"'"'s relevance score and correlation score for presentation to the user.

93. The system of claim 92, wherein the vector space is defined by a set of terms selected from the terms in the document database, each user profile vector and each document vector includes a plurality of vector components, each vector component corresponding to a weight of one of the terms.

94. The system of claim 92, wherein the vector operation is the determination of a cosine of an angle between the document vector and the user profile vector.

95. The system of claim 92, wherein the vector operation is a geometric operation determining a distance between the user profile vector and the document vector.

97. The system of claim 92, wherein each user profile vector comprises a plurality of user profile vector weights derived from the user'"'"'s interest in documents and each document vector comprises a plurality of document vector weights indicating the frequency of occurrence of the terms associated with the document vector weights in the document.

98. The system of claim 92, wherein the server receives a user rating of a document, and:

responsive to positive user rating, modifies the user profile vector of the user so that the user profile vector is more similar to the document vector of the user rated document; and

responsive to a negative user rating, modifies the user profile vector of the user so that the user profile vector is less similar to the document vector of the user rated document.

99. The system of claim 92, wherein the server receives a user rating of a document and modifies the user profile vector as a function of the user rating and the document vector of the user rated document.

100. The system of claim 92, wherein the server receives a user rating of a document indicating a user interest in the user rated document and modifies the user profile vector by determining which terms of the user rated document are significant and increasing the weights corresponding to the significant terms in the user profile vector.

101. The system of claim 92, wherein the document database includes a first document database and a second document database separate from the first document database, and the user profile vector associated with the user comprises a first user profile vector and a second user profile vector, the first and second user profile vectors corresponding to the first and second document databases, respectively, and the server:

updates the first user profile vector in response to a user rating of a document from the first document database; and

updates the second user profile vector in response to a user rating of a document from the second document database.

102. A method of presenting information items from an information item collection to a user, the method comprising:

accessing a user profile associated with the user;

for each information item in the information item collection;

determining a relevance score for the information item based on a relationship between the user profile and the information item; and

determining a correlation score between the user and other users corresponding to the information item; and

ranking the information items based on a combination of each information item'"'"'s relevance score and correlation score for presentation to the user.

103. A computer program product for presenting information items from an information item collection to a user, the computer program product stored on a computer readable medium and configured to perform a method comprising:

accessing a user profile associated with the user;

for each information item in the information item collection;

determining a relevance score for the information item based on a relationship between the user profile and the information item; and

determining a correlation score between the user and other users corresponding to the information item; and

ranking the information items based on a combination of each information item'"'"'s relevance score and correlation score for presentation to the user.

104. A system for presenting information items to a user, the information items stored in an information item database coupled to the system, the system comprising:

a user database storing a user profile associated with the user;

a server coupled to the information item database and the user database, the server identifying information items from the information item database and determining a relevance score for each of the identified information items based on a relationship between the user profile and the information item and determining a correlation score for each of the identified information items between the user and other users corresponding to the information item and ranking the identified information items based on a combination of each identified information item'"'"'s relevance score and correlation score for presentation to the user.