Personalized recommendation method based on graph auto-encoder

A recommendation method and auto-encoder technology, applied in neural learning methods, instruments, natural language data processing, etc., can solve problems such as not being able to identify user concerns well and difficult to identify.

Active Publication Date: 2021-02-09
ANHUI AGRICULTURAL UNIVERSITY
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AI Technical Summary

Problems solved by technology

[0006] (2) The same rating given by the user cannot identify the user's concerns well
For example, when users give 5 points to the same i...

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  • Personalized recommendation method based on graph auto-encoder
  • Personalized recommendation method based on graph auto-encoder
  • Personalized recommendation method based on graph auto-encoder

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Embodiment Construction

[0092] A personalized recommendation method based on a graph autoencoder, which uses the interaction between users and items to construct an adjacency matrix and normalize it, and uses a graph convolutional network to perform convolution operations to obtain hidden layer representations of nodes ; Use the comment text and item description text, use the graph attention network to aggregate the characteristics of neighbor nodes, so as to update the node information; use the attribute characteristics of users and items to construct a fully connected network to calculate the hidden layer features; calculate the above three networks Hidden layer features are concatenated to obtain new node information, and a fully connected network is constructed to encode this information. Then use the bilinear decoder to reconstruct the user's rating of the item. According to the reconstructed user's predicted score for each item, select items with high preference to generate a recommendation lis...

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Abstract

The invention discloses a personalized recommendation method based on a graph auto-encoder, and the method comprises the steps: building an adjacent matrix through the interaction between a user and an object, normalizing the adjacent matrix, carrying out convolution operation through a graph convolution network, and obtaining the hidden layer representation of each node; obtaining an initial feature vector of each node by taking a user comment text and an article description text as sources of node information, aggregating neighbor node features by using a graph attention network, and updating the node information; constructing a full-connection network by utilizing the attribute features of the user and articles to calculate to obtain hidden layer features; and splicing the hidden layerfeatures to obtain new node information, constructing a full-connection network, performing encoding, reconstructing scores of the user for the articles by using a bilinear decoder to serve as prediction scores, and generating a recommended article list by adopting TopN recommendation for the obtained prediction scores. Preference degree of the users to the articles can be analyzed more accurately, and the attention points of the users are found, so that more effective recommendation is carried out.

Description

technical field [0001] The invention relates to the technical field of text classification, deep learning, and recommendation system research, and in particular to a personalized recommendation method based on a graph autoencoder. Background technique [0002] With the continuous development of Internet technology, network information has grown explosively, but the rapid growth has also brought about the problem of information overload. Therefore, the importance of the process of accurately finding out what users are really interested in from a large amount of information is self-evident, and the proposal of the personalized recommendation algorithm is used to solve this problem, and it has gradually become an important aspect in the development prospect of the Internet. A big hot spot. [0003] Traditional recommendation methods such as collaborative filtering recommendation technology generally use user rating data on items to obtain user preferences. Although the algorit...

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Application Information

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IPC IPC(8): G06F16/9535G06F40/284G06N3/04G06N3/08
CPCG06F16/9535G06F40/284G06N3/08G06N3/045
Inventor 吴国栋刘玉良李方涂立静李景霞王伟娜
Owner ANHUI AGRICULTURAL UNIVERSITY
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