Knowledge social recommendation method, system and equipment based on graph neural network

A neural network and recommendation method technology, applied in biological neural network models, neural architectures, instruments, etc., can solve the problem of sparse scoring data recommending new user items, avoid data sparsity and cold start, avoid static and independent performance, improve the recommendation performance

Pending Publication Date: 2021-07-23
GUANGDONG UNIV OF TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0004] This application provides a knowledge social recommendation method, system and device based on a graph neural network, which is used to solve the problem of sparse rating data of users and items in existing recommendation systems and the cold start problem of recommending new user items, while avoiding user Staticity and independence of item processing to improve recommendation performance

Method used

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  • Knowledge social recommendation method, system and equipment based on graph neural network
  • Knowledge social recommendation method, system and equipment based on graph neural network
  • Knowledge social recommendation method, system and equipment based on graph neural network

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Embodiment 1

[0048] For ease of understanding, see Figure 1 to Figure 3 , the present application provides an embodiment of a knowledge social recommendation method based on a graph neural network, including:

[0049] Step 101, build user item bipartite graph, extract user vector and item entity vector by graph self-encoder, described graph self-encoder adopts PinSage algorithm to aggregate nodes.

[0050] Such as figure 1As shown, in the embodiment of this application, firstly construct a user-item bipartite graph, and use the PinSage algorithm to aggregate nodes to construct a graph autoencoder, which can bring controllable number of neighbor nodes and can be based on neighbor nodes in the process of aggregating neighbor nodes. Importance aggregation, extracting user vectors and item entity vectors. The information transfer from item j to user i is defined as: c ij is the regularization constant, W r is the parameter matrix based on the scoring level, is the feature vector of it...

Embodiment 2

[0085] see Figure 4 , the application provides an embodiment of a graph neural network-based knowledge social recommendation system, including:

[0086] The bipartite graph unit is used to construct a user-item bipartite graph, extract user vectors and item entity vectors through a graph autoencoder, and the graph autoencoder uses the PinSage algorithm to aggregate nodes.

[0087] The user interest extraction unit is used to input the user vector into the RNN network to obtain the user interest vector output by the RNN network.

[0088] The network node update unit is used to put the collected user friend interest vectors and user interest vectors into the social network graph, and use the graph attention mechanism to update the nodes of the social network graph to obtain newer interest vectors.

[0089] The splicing unit is used to splice the update interest vector and the user interest vector to obtain the user's final embedding vector.

[0090] The entity field represent...

Embodiment 3

[0129] This application provides an embodiment of a knowledge social recommendation device based on a graph neural network. The device includes a processor and a memory:

[0130] the memory is used to store the program code and transmit the program code to the processor;

[0131] The processor is used to execute the knowledge social recommendation method based on the graph neural network in Embodiment 1 according to the instructions in the program code.

[0132] The device provided in the embodiment of this application mines the influencing factors of users and items from social networks and knowledge graphs respectively, calculates the embedding vectors of users and items, remodels and decodes, and fuses knowledge graphs and social networks to build recommendation models, avoiding The data sparsity and cold start encountered in single information recommendation solve the problem of the existing recommendation system’s rating data sparseness of users and items and the cold sta...

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Abstract

The invention discloses a knowledge social recommendation method, system and device based on a graph neural network, and the method comprises the steps: respectively mining influence factors of a user and a project from a social network and a knowledge graph, calculating embedded vectors of the user and the project, carrying out the modeling and decoding again, fusing the knowledge graph and the social network, and constructing a recommendation model. According to the method, system and equipment, data sparsity and cold start encountered during single information recommendation are avoided, the problems of score data sparsity for users and projects and cold start for recommending new user projects in an existing recommendation system are solved, meanwhile, static performance and independence for processing the users and the projects are avoided, and the recommendation performance is improved.

Description

technical field [0001] This application relates to the technical field of network social information recommendation, in particular to a method, system and device for knowledge social recommendation based on graph neural network. Background technique [0002] With the proliferation of social networking sites, people can add friends, increase attention, and become fans through the Internet. These social behaviors constitute a huge social network. In order to make good use of social network resources and realize resource sharing and push, a recommendation system is used to push information related to user interests to users. The recommendation system mainly uses the user's behavior information on items to dig out the user's personalized needs, and actively provides users with information that meets their needs through the user's interest model, which has become an important research field for providing users with personalized services. a wide range of applications. [0003] I...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/9536G06N3/04G06F16/36G06Q50/00
CPCG06F16/9536G06F16/367G06Q50/01G06N3/044G06N3/045
Inventor 孙伟陈平华
Owner GUANGDONG UNIV OF TECH
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