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Knowledge graph driven personalized accurate recommendation method

A technology of knowledge graph and recommendation method, applied in the field of personalized and accurate recommendation driven by knowledge graph, can solve the problems of insufficient node representation and insufficient node interaction, and achieve the reduction of manual design process, accurate personalized recommendation results, and node representation. precise effect

Active Publication Date: 2020-04-03
NORTHWESTERN POLYTECHNICAL UNIV
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  • Application Information

AI Technical Summary

Problems solved by technology

Although these models have been proven to be effective on some public datasets, the interaction of nodes in the model is not sufficient, making the node representation in the knowledge graph inaccurate

Method used

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  • Knowledge graph driven personalized accurate recommendation method
  • Knowledge graph driven personalized accurate recommendation method
  • Knowledge graph driven personalized accurate recommendation method

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Embodiment Construction

[0032] The technical solutions of the present invention will be further described below in conjunction with the accompanying drawings.

[0033] A personalized and accurate recommendation method driven by knowledge graph, comprising the following steps:

[0034] S1. According to the user's historical behavior, obtain the relevant knowledge of the item from the knowledge base, and build a knowledge graph;

[0035] S2. For the constructed knowledge graph, initialize the vector representation of each node and connection and determine the receptive domain of the node;

[0036] S3. Generate training samples according to the user's historical behavior, and initialize the vector representations of all users and items;

[0037] S4. For each training sample, obtain the receptive domain of the corresponding entity in the knowledge map of the item in the training sample, and input the receptive domain and the sample as the graph neural network model to obtain the predicted value of the p...

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Abstract

The invention provides a knowledge graph driven personalized accurate recommendation method. The method comprises the steps of obtaining related knowledge of an article from a knowledge base accordingto historical behaviors of users, constructing a knowledge graph, initializing vector representation of each node and connection, and determining a feeling domain of each node; generating a trainingsample according to the historical behaviors of the users, and initializing vector representations of all the users and articles; obtaining the feeling domain of the corresponding entity of the articles in the training sample in the knowledge graph, and taking the feeling domains and the sample as graph neural network model input to obtain a possibility prediction value of interaction between theusers and the articles; optimizing model parameters by minimizing a loss function; and after the model optimization process is finished, sorting the prediction values of the possibility of interactionbetween a certain user and all the articles to obtain the recommendation list of the user. According to the method, the knowledge graph information is utilized, the sparsity of historical behavior information of an original user is made up, the users and the articles are described from the multi-dimensional perspective, and the personalized recommendation result is more accurate.

Description

technical field [0001] The present invention relates to the field of machine learning, in particular to a personalized and accurate recommendation method driven by knowledge graphs. Background technique [0002] With the development of information technology and the Internet, people have gradually entered the era of information overload from the era of information scarcity. In this era, both information consumers and information producers are facing a huge challenge: as an information consumer, how to find interesting information from a large amount of information is very difficult. As information producers, how to make the information they produce stand out and attract the attention of users is also a very difficult thing. Recommender systems are an important tool to resolve this contradiction. As an information filtering system, the recommendation system recommends to users the information that they are most likely to be interested in through the user's historical behavi...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/9535G06F16/36G06N3/04G06Q30/06
CPCG06F16/9535G06F16/367G06Q30/0631G06N3/045G06N3/08G06N3/042G06N5/02G06N5/04
Inventor 王柱汪子龙於志文郭斌周兴社
Owner NORTHWESTERN POLYTECHNICAL UNIV
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