Project recommendation method based on attribute coupled matrix decomposition

A technology of item recommendation and coupling matrix, applied in special data processing applications, instruments, data mining, etc., can solve the problem that item feature representation is not concise and effective, it is difficult to randomize user comment information, and it cannot well describe the similarity of user items, etc. question

Inactive Publication Date: 2017-01-04
INST OF BIG DATA RES AT YANCHENG OF NANJING UNIV OF POSTS & TELECOMM
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Problems solved by technology

However, item feature representations learned by probabilistic topic models may not be concise and effective, and inconsistent with the basic assumption of matrix factorization (only a small number of hidden f

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  • Project recommendation method based on attribute coupled matrix decomposition
  • Project recommendation method based on attribute coupled matrix decomposition
  • Project recommendation method based on attribute coupled matrix decomposition

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[0055] Below in conjunction with accompanying drawing, technical scheme of the present invention is described in further detail:

[0056] Such as figure 1 As shown, the present invention discloses an item recommendation method based on attribute coupling matrix decomposition, which includes the following steps:

[0057] Step 1), for the attribute information of the item to be recommended, the similarity between the items is calculated by using the coupling object similarity measurement index;

[0058] Step 2), according to the similarity of coupling objects between items, construct a regularization item containing item attribute information;

[0059] Step 3), on the basis of the matrix decomposition algorithm, combined with the regularization item containing the item information, using the gradient descent technique to learn the hidden feature vectors of the user and the item;

[0060] Step 4), according to the learned user and item hidden feature vectors, use the inner prod...

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Abstract

The invention discloses a project recommendation method based on attribute coupled matrix decomposition. The method comprises steps as follows: firstly, giving attribute information of projects and calculating the similarity between the projects with a coupled object similarity measuring index; then learning conceal eigenvectors of users and the projects with a matrix decomposition algorithm, during learning of the conceal eigenvectors of the projects, constructing a regularized term by means of the attribute information of the projects, and constraining the execution process of matrix decomposition, so that the projects with similar attribute information have similar conceal eigenvectors; finally, according to the learned conceal eigenvectors of the users and the projects, projecting scores of projects which are not scored by the users by use of inner products of the conceal eigenvectors of the users and the predicts, and providing personalized project recommendation for the users according to the predicted scores. The problems of similarity calculation of the projects, cold start of project terminals and recommendation accuracy in a recommendation system are solved with the method.

Description

technical field [0001] The invention relates to the technical field of data mining, in particular to an item recommendation method based on attribute coupling matrix decomposition. Background technique [0002] With the continuous development of Internet technology, it is becoming more and more difficult to find valuable relevant information from massive data, and users are faced with a serious problem of information overload. The recommendation system analyzes the user's historical activity data, mines the user's potential preferences, and provides users with personalized recommendation services. It has become an effective means to solve the problem of information overload, and has attracted extensive attention from academia and industry in recent years. Typical applications of recommendation systems include product recommendations from Amazon and Taobao, movie recommendations from Netflix, music recommendations from Last.fm, friend recommendations from LinkedIn, news recom...

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Application Information

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IPC IPC(8): G06F17/30
CPCG06F16/9535G06F2216/03
Inventor 余永红徐劲松赵卫滨蒋晶
Owner INST OF BIG DATA RES AT YANCHENG OF NANJING UNIV OF POSTS & TELECOMM
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