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Score recommendation method based on heterogeneous information

A recommendation method and technology of heterogeneous information, applied in the field of scoring recommendation based on heterogeneous information, to achieve the effect of good integration, maintaining preference diversity and uniqueness, and accurate recommendation

Active Publication Date: 2017-06-30
SUN YAT SEN UNIV
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AI Technical Summary

Problems solved by technology

However, such methods suffer from the fact that text messages are mostly short text
Moreover, the inaccuracy of text information also poses certain challenges to traditional methods

Method used

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  • Score recommendation method based on heterogeneous information
  • Score recommendation method based on heterogeneous information
  • Score recommendation method based on heterogeneous information

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Embodiment Construction

[0035] The technical solutions of the present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0036] Such as figure 1 As shown, the present invention provides a scoring recommendation method based on heterogeneous information, which specifically includes the following steps:

[0037] S1. Obtain the user ID, item ID, rating information, comment information and item description information of the item;

[0038] S2. Converting the rating information, review information and item description information into rating vectors, comment vectors and item description vectors respectively;

[0039] S3. Substituting the rating vector, review vector and item description vector into the following loss function, and solving it by gradient descent; wherein, the review vector and item description vector are constants, and the rating vector is a variable;

[0040]

[0041] Among them, u represents the user feature vector, ...

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Abstract

The invention provides a score recommendation method based on heterogeneous information. The method includes the following steps: 1, obtaining user ID, article ID, score information, comment information and article description information of an article; 2, converting the score information, the comment information and the article description information into a score vector, a comment vector and an article description vector respectively; 3, introducing the score vector, the comment vector and the article description vector into a loss function, and solving is conducted by the adoption of a gradient descent method; 4, according to final vectors obtained through calculation in the step 3, a user recommendation degree of the article is obtained through equations. By the adoption of the score recommendation method, the short text problem of text messages is solved, different information under different space expression can be integrated more effectively, so that the score information, the comment information and the article information are fused better, and finally, more accurate recommendation is made.

Description

technical field [0001] The invention relates to the field of data mining, and relates to a rating recommendation method based on heterogeneous information. Background technique [0002] With the rapid development of information technology, various technologies of big data have emerged as the times require, but the problem of information overload is still becoming more and more serious. As an important tool to solve information overload, the rating recommendation system recommends more accurate items to users by analyzing the behavior information between users and items, and the description information of users and items, so as to maximize the value of information. [0003] In rating recommendation systems, the challenge is mainly the sparsity of data. In addition to rating information, text information such as comments has become the best supplement. However, in the research on integrating rating and review information into rating prediction recommendation system, most of ...

Claims

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Application Information

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IPC IPC(8): G06F17/27G06F17/30
CPCG06F16/9535G06F40/284
Inventor 何俊华卓汉逵
Owner SUN YAT SEN UNIV
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