Eigenvector Calculation Method and Device
A technology of eigenvectors and calculation methods, applied in the field of eigenvector calculation methods and devices based on item recommendation, can solve problems such as matrix decomposition is difficult to complete, and achieve the effect of reducing the number of matrix elements and reducing the amount of calculation
Active Publication Date: 2019-05-07
GUANGZHOU KUGOU COMP TECH CO LTD
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Problems solved by technology
[0011] In order to solve the problem that the matrix decomposition is difficult to complete due to the large number of users, the embodiment of the present invention provides a method and device for calculating feature vectors based on item recommendation
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[0050] In order to make the object, technical solution and advantages of the present invention clearer, the implementation manner of the present invention will be further described in detail below in conjunction with the accompanying drawings.
[0051] The eigenvector calculation method provided by each embodiment of the present disclosure can be realized by a terminal installed with at least one application program, and the terminal can be a mobile phone, a tablet computer, an e-book reader, an MP3 (Moving Picture Experts Group Audio Layer III, a moving picture expert Compression standard audio level 3) players, MP4 (Moving Picture Experts Group Audio Layer IV, moving picture experts compression standard audio level 4) electronic devices such as players, portable computers and desktop computers.
[0052] The feature vector calculation methods provided by various embodiments of the present disclosure can be applied to an item recommendation system implemented by computer softwa...
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The invention discloses a feature vector calculation method and device, and belongs to the field of item recommending. The method comprises the steps that items i are used for representing users of the same class liking the items i to construct a row in a scoring matrix, items j are used for constructing a column in the scoring matrix, the similarity between the items i and the items j represents scores of the users of the same class liking the items i for the items j to construct matrix elements in the scoring matrix, and matrix decomposition is performed on the scoring matrix according to a target function to obtain a first matrix and a second matrix; the first matrix comprises a feature vector of the users of each class, and the second matrix comprises a feature vector of the items of the same kind. The problems that in the prior art, the users are used for constructing the row of the scoring matrix and matrix decomposition is hard to complete due to the fact that the number of users is large are solved; the effects that the items are used for replacing the users of the same class for constructing the row of the scoring matrix, the number of matrix elements in the scoring matrix is decreased and the calculated amount in the matrix decomposition process is decreased are achieved.
Description
technical field [0001] Embodiments of the present invention relate to the field of item recommendation, in particular to a method and device for calculating feature vectors based on item recommendation. Background technique [0002] The feature vector of the song needs to be used in the song recommendation process. A feature vector is a vector used to represent features of an item. The feature vectors of the songs need to be precomputed. [0003] The matrix factorization method is a highly accurate calculation method for calculating the feature vector of a song. The matrix decomposition method needs to construct an N*M rating matrix between users and songs. [0004] [0005] Wherein, each row in the scoring matrix corresponds to a user, and each column corresponds to a song. w ij Indicates the rating value of the i-th user on the j-th item. [0006] Using the matrix decomposition method, the N*M scoring matrix can be decomposed into an N*K matrix and a K*M matrix. ...
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IPC IPC(8): G06F17/16
CPCG06F17/16
Inventor 江海金
Owner GUANGZHOU KUGOU COMP TECH CO LTD