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Social interest recommendation method and system based on graph convolution matrix decomposition

A matrix decomposition and convolution technology, applied in the field of social interest recommendation methods and systems, can solve problems such as the inability to capture influences and sparse data of collaborative filtering algorithms, and achieve the effects of enhancing interpretability, alleviating data sparse problems, and advanced recommendation performance

Pending Publication Date: 2020-08-11
SHANDONG NORMAL UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, with the rapid development of the website, the number of users and items increases sharply, which leads to the serious data sparse problem faced by the collaborative filtering algorithm: the number of products evaluated by users in the website only accounts for a very small part of the total number
Therefore, it is also impossible to capture the impact of the social communication process on users.

Method used

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  • Social interest recommendation method and system based on graph convolution matrix decomposition
  • Social interest recommendation method and system based on graph convolution matrix decomposition
  • Social interest recommendation method and system based on graph convolution matrix decomposition

Examples

Experimental program
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Embodiment 1

[0039] This embodiment provides a social interest recommendation method based on graph convolution matrix decomposition;

[0040] like figure 1 As shown, the social interest recommendation method based on graph convolution matrix decomposition includes:

[0041] S101: Obtain the user social item scoring matrix, user social adjacency matrix and user comment text feature matrix of the user to be recommended;

[0042] S102: Input the user social item score matrix, user social adjacency matrix and user comment text feature matrix of the user to be recommended into the trained graph convolution matrix decomposition model; the trained graph convolution matrix decomposition model is output to be recommended User latent feature matrix and item latent feature matrix of users;

[0043] S103: Recommend potential items to the user to be recommended according to the user latent feature matrix and the item latent feature matrix.

[0044] As one or more embodiments, in S101, the user soci...

Embodiment 2

[0162] This embodiment provides a social interest recommendation system based on graph convolution matrix decomposition;

[0163] like Figure 5 As shown, the social interest recommendation system based on graph convolution matrix factorization includes:

[0164] The acquisition module is configured to: acquire the user social item scoring matrix, the user social adjacency matrix and the user comment text feature matrix of the user to be recommended;

[0165] The recommendation module is configured to: input the user social item score matrix, user social adjacency matrix and user comment text feature matrix of the user to be recommended into the trained graph convolution matrix decomposition model; the trained graph convolution matrix Decompose the model and output the user latent feature matrix and item latent feature matrix of the user to be recommended;

[0166] The output module is configured to: recommend potential items to users to be recommended according to the user ...

Embodiment 3

[0171] This embodiment also provides an electronic device, including: one or more processors, one or more memories, and one or more computer programs; wherein, the processor is connected to the memory, and the one or more computer programs are programmed Stored in the memory, when the electronic device is running, the processor executes one or more computer programs stored in the memory, so that the electronic device executes the method described in Embodiment 1 above.

[0172] It should be understood that in this embodiment, the processor can be a central processing unit CPU, and the processor can also be other general-purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate array FPGA or other programmable logic devices , discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor, or the processor may be any conventional processor, o...

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Abstract

The invention discloses a social interest recommendation method and system based on graph convolution matrix decomposition. The method comprises the steps of obtaining a user social project scoring matrix, a user social adjacency matrix and a user comment text feature matrix of a to-be-recommended user; inputting the user social project scoring matrix, the user social adjacency matrix and the usercomment text feature matrix of the to-be-recommended user into a trained graph convolution matrix decomposition model; outputting a user potential feature matrix and a project potential feature matrix of the to-be-recommended user by the trained graph convolution matrix decomposition model ; and recommending potential projects to the to-be-recommended user according to the user potential featurematrix and the project potential feature matrix.

Description

technical field [0001] The present disclosure relates to the technical field of item recommendation, in particular to a social interest recommendation method and system based on graph convolution matrix decomposition. Background technique [0002] The statements in this section merely mention background art related to the present disclosure and do not necessarily constitute prior art. [0003] As an effective means of information filtering, the recommendation system has become an essential technology for major e-commerce websites, and is widely used to recommend personalized content for users. Among them, collaborative filtering (Collaborative Filtering, CF) is one of the most widely used information filtering methods in recommendation systems. Its main task is to predict user preferences by mining the historical records of similar users or items (mainly referring to the user-item rating matrix). However, with the rapid development of the website, the number of users and i...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/9536G06F17/16G06Q50/00G06N3/04G06N3/08
CPCG06F16/9536G06F17/16G06Q50/01G06N3/084G06N3/045
Inventor 王新华杨新新郭磊刘方爱
Owner SHANDONG NORMAL UNIV
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