Information processing apparatus, information processing method, program for implementing information processing method, information processing system, and method for information processing system
First Claim
1. An information processing apparatus comprising:
- modification means for acquiring M information sets each including N pieces of individual information and modifying at least partially the N pieces of individual information of each of the M information sets such that correlations among the N pieces of individual information are emphasized, where N is an integer equal to or greater than 2 and M is an integer equal to or greater than 1;
generation means for generating a reference information set including N pieces of individual information for use as a reference in a calculation of similarity, from the M information sets each including N pieces of individual information modified by the modification means; and
similarity calculation means for acquiring, as a comparative information set, a new information set including N individual information elements and calculating the similarity of the comparative information set with respect to the reference information set produced by the generation means.
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Abstract
An information processing apparatus selects a proper content that well matches preference of a user and recommends it. A matrix calculator acquires M (one or more) feature vectors CCV whose elements are given by weight values assigned to a total of N (two or more) pieces of content meta information and context information. The matrix calculator produces a matrix CCM whose columns are given by the M feature vectors CCV and converts it into an approximate matrix CCM* by modifying the weight values of the respective elements of the M feature vectors CCV such that correlations of elements among the M feature vectors CCV are emphasized. Based on the approximate matrix CCM*, a user preference vector (UPV) generator produces a user preference vector UPV*. A matching unit calculates similarity between the user preference vector UPV* and a feature vector CCV produced from new content meta information or context information.
112 Citations
43 Claims
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1. An information processing apparatus comprising:
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modification means for acquiring M information sets each including N pieces of individual information and modifying at least partially the N pieces of individual information of each of the M information sets such that correlations among the N pieces of individual information are emphasized, where N is an integer equal to or greater than 2 and M is an integer equal to or greater than 1; generation means for generating a reference information set including N pieces of individual information for use as a reference in a calculation of similarity, from the M information sets each including N pieces of individual information modified by the modification means; and similarity calculation means for acquiring, as a comparative information set, a new information set including N individual information elements and calculating the similarity of the comparative information set with respect to the reference information set produced by the generation means. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31)
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32. An information processing method comprising:
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a modification step of acquiring M information sets each including N pieces of individual information and modifying at least partially the N pieces of individual information of each of the M information sets such that correlations among the N pieces of individual information are emphasized, where N is an integer equal to or greater than 2 and M is an integer equal to or greater than 1; a generation step of generating a reference information set including N pieces of individual information for use as a reference in a calculation of similarity, from the M information sets each including N pieces of individual information modified in the modification step; and a similarity calculation step of acquiring, as a comparative information set, a new information set including N individual information elements and calculating the similarity of the comparative information set with respect to the reference information set produced in the generation step.
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33. A program executed by a computer, comprising:
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a modification step of acquiring M information sets each including N pieces of individual information and modifying at least partially the N pieces of individual information of each of the M information sets such that correlations among the N pieces of individual information are emphasized, where N is an integer equal to or greater than 2 and M is an integer equal to or greater than 1; a generation step of generating a reference information set including N pieces of individual information for use as a reference in a calculation of similarity, from the M information sets each including N pieces of individual information modified in the modification step; and a similarity calculation step of acquiring, as a comparative information set, a new information set including N individual information elements and calculating the similarity of the comparative information set with respect to the reference information set produced in the generation step.
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34. An information processing apparatus comprising:
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vectorization means for, each time a content is used by a user, acquiring an information set including N pieces of individual information associated with the content, assigning a weight value to each of the N pieces of individual information, and producing as a feature vector for the content a vector whose elements are given by the weight values assigned to the respective N pieces of individual information; matrix generation means for, when M feature vectors are produced by the vectorization means, producing a first matrix having N rows and M columns such that the elements thereof are given by the M feature vectors, performing an operation on the first matrix so as to convert the first matrix into a second matrix whose N×
M elements are at least partially modified such that correlations among N row elements of each of the M columns are emphasized;reference vector generation means for producing a reference vector including N elements for use as a reference in calculation of similarity, from the M columns of the second matrix produced by the matrix generation means; candidate acquisition means for, each time a reference vector is produced by the reference vector generation means, acquiring a candidate for a content to be recommended to a user, based on the reference vector; similarity calculation means for acquiring an information set including N pieces of individual information associated with a new content, assigning a weight value to each of the N pieces of individual information, produces a comparative vector whose elements are given by the weight values assigned to the N pieces of individual information, and calculates the similarity between the comparative vector and one or more reference vectors produced by the reference vector generation means; and presentation means for selecting, from candidate contents acquired by the candidate acquisition means, a candidate content corresponding to a reference vector whose similarity calculated by the similarity calculation means is equal to or higher than a threshold value and presenting the selected candidate content as a recommended content to the user.
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35. An information processing method comprising:
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a vectorization step of, each time a content is used by a user, acquiring an information set including N pieces of individual information associated with the content, assigning a weight value to each of the N pieces of individual information, and producing as a feature vector for the content a vector whose elements are given by the weight values assigned to the respective N pieces of individual information; a matrix generation step of, when M feature vectors are produced in the vectorization step, producing a first matrix having N rows and M columns such that the elements thereof are given by the M feature vectors, performing an operation on the first matrix so as to convert the first matrix into a second matrix whose N×
M elements are at least partially modified such that correlations among N row elements of each of the M columns are emphasized;a reference vector generation step of producing a reference vector including N elements for use as a reference in calculation of similarity, from the M columns of the second matrix produced in the matrix generation step; a candidate acquisition step of, each time a reference vector is produced in the reference vector generation step, acquiring a candidate for a content to be recommended to a user, based on the reference vector; a similarity calculation step of acquiring an information set including N pieces of individual information associated with a new content, assigning a weight value to each of the N pieces of individual information, produces a comparative vector whose elements are given by the weight values assigned to the N pieces of individual information, and calculates the similarity between the comparative vector and one or more reference vectors produced in the reference vector generation step; and a presentation step of selecting, from candidate contents acquired in the candidate acquisition step, a candidate content corresponding to a reference vector whose similarity calculated by the similarity calculation means is equal to or higher than a threshold value and presenting the selected candidate content as a recommended content to the user.
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36. A program executed by a computer, comprising:
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a vectorization step of, each time a content is used by a user, acquiring an information set including N pieces of individual information associated with the content, assigning a weight value to each of the N pieces of individual information, and producing as a feature vector for the content a vector whose elements are given by the weight values assigned to the respective N pieces of individual information; a matrix generation step of, when M feature vectors are produced in the vectorization step, producing a first matrix having N rows and M columns such that the elements thereof are given by the M feature vectors, performing an operation on the first matrix so as to convert the first matrix into a second matrix whose N×
M elements are at least partially modified such that correlations among N row elements of each of the M columns are emphasized;a reference vector generation step of producing a reference vector including N elements for use as a reference in calculation of similarity, from the M columns of the second matrix produced in the matrix generation step; a candidate acquisition step of, each time a reference vector is produced in the reference vector generation step, acquiring a candidate for a content to be recommended to a user, based on the reference vector; a similarity calculation step of acquiring an information set including N pieces of individual information associated with a new content, assigning a weight value to each of the N pieces of individual information, produces a comparative vector whose elements are given by the weight values assigned to the N pieces of individual information, and calculates the similarity between the comparative vector and one or more reference vectors produced in the reference vector generation step; and a presentation step of selecting, from candidate contents acquired in the candidate acquisition step, a candidate content corresponding to a reference vector whose similarity calculated by the similarity calculation means is equal to or higher than a threshold value and presenting the selected candidate content as a recommended content to the user.
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37. An information processing apparatus comprising:
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vectorization means for, each time a content is used by a user, acquiring an information set including N pieces of individual information associated with the content, assigning a weight value to each of the N pieces of individual information, and producing as a feature vector for the content a vector whose elements are given by the weight values assigned to the respective N pieces of individual information; matrix generation means for, when M feature vectors are produced by the vectorization means, producing a first matrix having N rows and M columns such that the elements thereof are given by the M feature vectors, performing an operation on the first matrix so as to convert the first matrix into a second matrix whose N×
M elements are at least partially modified such that correlations among N row elements of each of the M columns are emphasized; andgenre setting means for calculating similarity among the M columns of the second matrix produced by the matrix generation means, classifying each of the M columns based on the calculated similarity, and setting each class obtained in the classification as a genre. - View Dependent Claims (38, 39)
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40. An information processing method comprising:
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a vectorization step of, each time a content is used by a user, acquiring an information set including N pieces of individual information associated with the content, assigning a weight value to each of the N pieces of individual information, and producing as a feature vector for the content a vector whose elements are given by the weight values assigned to the respective N pieces of individual information; a matrix generation step of, when M feature vectors are produced in the vectorization step, producing a first matrix having N rows and M columns such that the elements thereof are given by the M feature vectors, performing an operation on the first matrix so as to convert the first matrix into a second matrix whose N×
M elements are at least partially modified such that correlations among N row elements of each of the M columns are emphasized; anda genre setting step of calculating similarity among the M columns of the second matrix produced in the matrix generation step, classifying each of the M columns based on the calculated similarity, and setting each class obtained in the classification as a genre.
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41. A program executed by a computer, comprising:
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a vectorization step of, each time a content is used by a user, acquiring an information set including N pieces of individual information associated with the content, assigning a weight value to each of the N pieces of individual information, and producing as a feature vector for the content a vector whose elements are given by the weight values assigned to the respective N pieces of individual information; a matrix generation step of, when M feature vectors are produced in the vectorization step, producing a first matrix having N rows and M columns such that the elements thereof are given by the M feature vectors, performing an operation on the first matrix so as to convert the first matrix into a second matrix whose N×
M elements are at least partially modified such that correlations among N row elements of each of the M columns are emphasized; anda genre setting step of calculating similarity among the M columns of the second matrix produced in the matrix generation step, classifying each of the M columns based on the calculated similarity, and setting each class obtained in the classification as a genre.
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42. An information processing system including a server and a client used by a user, comprising:
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vectorization means for, each time a content is used by the user, acquiring an information set including N pieces of individual information associated with the content, assigning a weight value to each of the N pieces of individual information, and producing as a feature vector for the content a vector whose elements are given by the weight values assigned to the respective N pieces of individual information; matrix generation means for, when M feature vectors are produced by the vectorization means, producing a first matrix having N rows and M columns such that the elements thereof are given by the M feature vectors, performing an operation on the first matrix so as to convert the first matrix into a second matrix whose N×
M elements are at least partially modified such that correlations among N row elements of each of the M columns are emphasized;reference vector generation means for producing a reference vector including N elements for use as a reference in calculation of similarity, from the M columns of the second matrix produced by the matrix generation means; and similarity calculation means for calculating similarity by acquiring a new information set including N pieces of individual information, assigning weight values to the respective N pieces of individual information, producing a comparative feature vector whose elements are given by the weight values assigned to the respective N pieces of individual information, and calculating similarity between the comparative vector and the reference vector produced by the reference vector generation means, wherein of the vectorization means, the matrix generation means, the reference vector generation means, and the similarity calculation means, at least the matrix generation means is included in the server.
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43. An information processing method for an information processing system including a server and a client used by a user, comprising:
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a vectorization step of, each time a content is used by the user, acquiring an information set including N pieces of individual information associated with the content, assigning a weight value to each of the N pieces of individual information, and producing as a feature vector for the content a vector whose elements are given by the weight values assigned to the respective N pieces of individual information; a matrix generation step of, when M feature vectors are produced in the vectorization step, producing a first matrix having N rows and M columns such that the elements thereof are given by the M feature vectors, performing an operation on the first matrix so as to convert the first matrix into a second matrix whose N×
M elements are at least partially modified such that correlations among N row elements of each of the M columns are emphasized;a reference vector generation step of producing a reference vector including N elements for use as a reference in calculation of similarity, from the M columns of the second matrix produced in the matrix generation step; and a similarity calculation step of calculating similarity comprising the substeps of acquiring a new information set including N pieces of individual information, assigning weight values to the respective N pieces of individual information, producing a comparative feature vector whose elements are given by the weight values assigned to the respective N pieces of individual information, and calculating similarity between the comparative vector and the reference vector produced in the reference vector generation step, wherein of the vectorization step, the matrix generation step, the reference vector generation step, and the similarity calculation step, at least the matrix generation step is performed by the server.
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Specification