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Neighborhood exploration method based on heterogeneous graph neural network

A neural network and heterogeneous technology, applied in the field of neighborhood exploration based on heterogeneous graph neural network, can solve problems such as poor extraction of potential features of users or products, weakening of structural equivalence, fragmentation, etc., to improve reliability Interpretability and Accuracy Effects

Pending Publication Date: 2021-06-18
UNIV OF ELECTRONIC SCI & TECH OF CHINA
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AI Technical Summary

Problems solved by technology

Most recommendation algorithms based on heterogeneous information networks use meta-path-based similarity to improve accuracy, and cannot extract latent features of users or items well
[0004] Patent No. 202010094718.7, proposes a movie recommendation method based on network embedding of meta-paths, recommends different meta-paths with different semantic information in heterogeneous information networks, adopts random walk method based on meta-paths, and obtains more Semantic information, thereby improving the accuracy of recommendation; however, its disadvantage is that it splits the heterogeneous graph network structure, resulting in weakened structural equivalence between nodes

Method used

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  • Neighborhood exploration method based on heterogeneous graph neural network
  • Neighborhood exploration method based on heterogeneous graph neural network
  • Neighborhood exploration method based on heterogeneous graph neural network

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Embodiment Construction

[0019] In order to have a clearer understanding of the technical features, purposes and effects of the present invention, the specific implementation manners of the present invention will now be described with reference to the accompanying drawings.

[0020] In this example, if figure 1 As shown, a neighborhood exploration method based on a heterogeneous graph neural network includes the following steps:

[0021] Step 1: Define a parameter α used to guide the direction of exploration;

[0022] Step 2: In a given heterogeneous graph ɡ and meta-path Φ, for node V i Each immediate neighbor of V j , with a probability of α do not perform any operation, and jump to the next direct neighbor node; with a probability of 1-α, carry out biased walk sampling, and use the meta-path neighbors obtained by the walk sampling Replace the original immediate neighbor V j ;

[0023] Step 3: With the probability of α, add the immediate neighbors to the neighbor set; with the probability of 1...

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Abstract

The invention discloses a neighborhood exploration method based on a heterogeneous graph neural network. The neighborhood exploration method comprises the following steps: step 1, defining a parameter alpha for guiding an exploration direction; 2, in the given heterogeneous graph and meta path phi, for each direct neighbor of the node, no carrying out operation on the probability of alpha, and skipping to the next direct neighbor node; carrying out biased migration sampling when the probability is 1-alpha, and replacing original direct neighbors with meta-path neighbors obtained by migration sampling; 3, adding the direct neighbors into a neighbor set according to the probability of alpha; and adding the meta-path neighbors into the neighbor set according to the probability of 1-alpha. According to the method, two exploration strategies of depth-first exploration and breadth-first exploration are smoothly spliced to adapt to different heterogeneous network structures, and specific semantic neighbors are captured, so that the interpretability and accuracy of a recommendation system are improved.

Description

technical field [0001] The invention relates to the field of computer network analysis, in particular to a neighborhood exploration method based on a heterogeneous graph neural network. Background technique [0002] In recent years, recommender systems have played an increasingly important role in various Internet products, as it can help users discover items of interest, such as movies, commodities, etc., in a huge database. The recommendation system is used to mine the user's historical behavior, and establish respective feature matrices according to the characteristics of users and products. Traditional recommendation methods, such as collaborative filtering, mainly use neighbor users or neighbor items with high similarity to predict the ratings of target user candidate items. A common practice is to first construct a user-product rating matrix, then calculate the similarity to determine the neighbor set, and finally predict the rating to generate a recommendation list. ...

Claims

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Application Information

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IPC IPC(8): G06N3/04G06F16/9535
CPCG06F16/9535G06N3/047
Inventor 张凤荔张志扬王瑞锦周世杰
Owner UNIV OF ELECTRONIC SCI & TECH OF CHINA
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