Hybrid recommendation method based on graph convolutional neural network

A convolutional neural network and mixed recommendation technology, applied in neural learning methods, biological neural network models, instruments, etc., can solve the problems of traditional methods such as data sparseness, heavy feature engineering, and failure to satisfy users, so as to improve accuracy and diversity performance, alleviate heavy feature engineering, and alleviate the effect of data sparsity

Active Publication Date: 2020-01-10
HANGZHOU DIANZI UNIV
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Problems solved by technology

[0003] However, traditional methods usually suffer from problems such as data sparsity, heavy feature engineering, etc., and cannot meet users' needs

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  • Hybrid recommendation method based on graph convolutional neural network
  • Hybrid recommendation method based on graph convolutional neural network
  • Hybrid recommendation method based on graph convolutional neural network

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[0031] In order to describe the present invention more specifically, the technical solutions of the present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0032] The hybrid recommendation method based on graph convolutional neural network of the present invention comprises the following steps:

[0033] (1). Collect behavioral data of all users U={u 1 , u 2 ,...,u |U|} is the set of all users, where the behavior data of user u∈U is expressed as a sequence Bu={(i 1 ,t 1 ),(i 2 ,t 2 ),…,(i m ,t m )}, (i j ,t j ) represent the items interacted by the user and the interaction time respectively, I={i 1 ,i 2 ,...,i |M|} is the collection of all items. Collect attribute data A of all items in I, including but not limited to categories, tags, metadata and other information.

[0034] (2). Construct a heterogeneous information graph G=(N,E) based on the behavior data of all users and the attribute data...

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Abstract

The invention discloses a hybrid recommendation method based on a graph convolutional neural network. The method comprises the following steps of collecting the behavior data of a user to an article and the attribute information of the article; modeling the collected data into a heterogeneous information graph, and learning the feature vector representation of each node by utilizing the graph convolutional neural network; carrying out hybrid recommendation based on the user and the item feature vectors. According to the method, multiple data including the behavior data of the user to the articles and the article attribute information are mainly utilized to obtain the feature vector representation of the user and the articles, and the hybrid recommendation based on the feature vectors is implemented, so that the influence of a data sparsity problem and a heavy feature engineering problem is relieved, then the recommendation effect is improved, and the user satisfaction is improved.

Description

technical field [0001] The invention belongs to the technical fields of data mining, information retrieval and recommendation, and in particular relates to a hybrid recommendation method based on a graph convolutional neural network. Background technique [0002] With the rapid development of information technology, people can enjoy network services and content conveniently, but at the same time, they also face the problem of information overload brought by massive data, making it difficult to find the content they are interested in. The recommendation system can help users find relevant data from massive online information to meet user needs. Accurately obtaining the characteristics of items and efficiently calculating their similarity is one of the cores of realizing a personalized recommendation system. [0003] However, traditional methods usually suffer from problems such as data sparsity, heavy feature engineering, etc., and cannot meet users' needs. Therefore, how to...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/9535G06Q30/06G06N3/08
CPCG06N3/08G06Q30/0631G06F16/9535
Inventor 王东京张新俞东进
Owner HANGZHOU DIANZI UNIV
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