Commodity personalized recommendation method and system based on community discovery and graph neural network

A technology of community discovery and neural network, which is applied in the field of commodity personalized recommendation based on community discovery and graph neural network, can solve the problem of sparse total quantity of commodities, achieve the effects of reducing sparsity, improving recommendation effect, and avoiding insufficient application

Active Publication Date: 2021-11-12
CENT SOUTH UNIV
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[0005] The present invention provides a product personalized recommendation method and system based on community discovery and graph neural network to solve th

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  • Commodity personalized recommendation method and system based on community discovery and graph neural network
  • Commodity personalized recommendation method and system based on community discovery and graph neural network
  • Commodity personalized recommendation method and system based on community discovery and graph neural network

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[0054] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings, but the present invention can be implemented in a variety of different ways of claim defined and covered.

[0055] See figure 1 Community-based personalized recommendation methods based on community discovery and nerve networks, including:

[0056] S1: Get interactive information of the user and the product, mapping the user and commodity mapping as a node, the interactive information is mapped to the side, and the user-interactive network is built; community discovery in the built user interaction network, re-imparts the properties of the user node according to the user of the user .

[0057] S101: Get the interaction information of the user and the product, according to its two-point network, users and commodity maps, and the user and commodity map are mapped to the node. If the user is interacting with the goods (purchase / click operation), The side is...

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Abstract

The invention discloses a commodity personalized recommendation method and system based on community discovery and a graph neural network. The method comprises the steps of obtaining the interaction information of a user and a commodity, and constructing a user interaction network with the mapping of the user and the commodity as nodes and the mapping of the interaction information as edges, conducting community discovery in the constructed user interaction network, and endowing attributes of user nodes again according to communities to which users belong, reconstructing a bipartite graph for user nodes with attributes and commodity interaction information, and mapping the user nodes and commodity nodes into vector representation, inputting the user vector representation, the commodity vector representation and the constructed bigraph into an embedded propagation layer for graph representation learning, and optimizing the user vector representation and the commodity vector representation, performing inner product operation on the optimized user vector and the commodity vector, and predicting the probability that the user clicks the commodity according to an inner product result, and obtaining a personalized commodity recommendation sequence for the user. According to the method, the recommended cold start problem can be relieved, and a good recommendation effect is achieved.

Description

technical field [0001] The present invention relates to the technical field of commodity recommendation, in particular to a method and system for personalized commodity recommendation based on community discovery and graph neural network. Background technique [0002] In recent years, with the rapid development of technologies such as cloud computing, big data, and the Internet of Things, the emergence of various applications in the Internet space has triggered an explosive growth in data scale. Big data contains rich value and great potential, which will bring transformative development to human society, but at the same time, it will also bring about a serious problem of "information overload". Valuable information has become a key problem in the current development of big data. [0003] As an effective method to solve the "information overload" problem, the recommendation system has become a hot spot in academia and industry and has been widely used, forming a lot of rela...

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Application Information

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IPC IPC(8): G06F16/9536G06Q30/06G06N3/04G06N3/08G06Q50/00
CPCG06F16/9536G06Q30/0631G06N3/08G06Q50/01G06N3/047G06N3/048G06N3/045
Inventor 王斌候正昂盛津芳
Owner CENT SOUTH UNIV
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