Personalized recommendation method based on multi-view knowledge graph attention network

A knowledge map and attention technology, applied in the field of knowledge map and recommendation system, can solve the problems of inability to fully capture the feature information of the knowledge map and low accuracy of the recommendation system

A knowledge map and attention technology, applied in the field of knowledge map and recommendation system, can solve the problems of inability to fully capture the feature information of the knowledge map and low accuracy of the recommendation system

CN113378048AActive Publication Date: 2021-09-10ZHEJIANG UNIV OF TECH
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  • Personalized recommendation method based on multi-view knowledge graph attention network
  • Personalized recommendation method based on multi-view knowledge graph attention network
  • Personalized recommendation method based on multi-view knowledge graph attention network

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Experimental program
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Embodiment Construction

[0096] Below in conjunction with accompanying drawing, the present invention will be further described

[0097] refer to figure 1 , figure 2 , image 3 , a personalized recommendation method based on multi-view knowledge graph attention network, including the following steps:

[0098] 1) Build a multi-view knowledge map

[0099] Divide the knowledge map, and divide the multi-view knowledge map according to the relationship between nodes, such as figure 1 The shown social graph, relationship graph, and interaction graph respectively capture user social relationship change features, object relationship change features, and user object interest change features;

[0100] 2) Construct the knowledge map adjacency matrix

[0101] For the three kinds of multi-view knowledge graphs in step (1), the adjacency matrix is ​​constructed according to the link relationship between the nodes in the graph, to figure 2 Take the knowledge graph as an example, figure 2 (a) is a knowledge...

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Abstract

The invention discloses a personalized recommendation method based on a multi-view knowledge graph attention network. The method comprises the following steps: 1) constructing a multi-view knowledge graph; 2) constructing a direct adjacent matrix and an indirect adjacent matrix corresponding to the multi-view knowledge graph according to the multi-view knowledge graph; 3) designing a drawing attention network, and learning multi-view feature information based on an attention mechanism to obtain vector representations of a user and an object; 4) extracting social contact, relation and interaction feature information of the knowledge graph based on the graph attention network; and 5) training and learning the knowledge graph attention network to obtain final vector representation of the user and the object. According to the invention, the accuracy of a recommendation system is improved by learning the multi-view features in the knowledge graph.

Description

technical field [0001] The invention relates to the fields of knowledge graphs, recommendation systems, etc., and in particular provides a personalized recommendation method based on multi-view knowledge graph attention networks. Background technique [0002] With the rapid development of Internet technology, users can choose more and more information, but it also makes users face the problem of "information overload" caused by the large amount of information. How to better provide users with interesting information has become a research hotspot, and the recommendation system is an effective way to solve this problem. Objects are recommended to users to meet their individual needs. [0003] Nowadays, the main problems faced by recommender systems are data sparsity and cold start problem. Data sparsity means that for a large number of users and recommended objects, there is only a small amount of interactive information, making traditional recommendation methods inefficient...

Claims

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Application Information

Patent Timeline
10 Sep 2021
Publication
CN113378048A
IPC
G06F16/9535; G06F16/36; G06Q50/00; G06N3/04; G06N3/08; G06K9/62
CPC
G06F16/9535; G06F16/367; G06Q50/01; G06N3/08; G06N3/047; G06N3/045; G06F18/253
Inventors
张元鸣; 徐洲帅