Social recommendation method based on heterogeneous graph neural network

Abstract

The invention discloses a social recommendation method based on a heterogeneous graph neural network. The method comprises the following steps that: a local heterogeneous graph is constructed according to local data on a client; the client requests model parameters from the server, and performs embedded learning on the local heterogeneous graph by using the graph attention network model so as to process the isomerism of the local graph and the personalized information of the client; a user is associated with a client, after a pseudo item label is added to the client, the gradient of the client is calculated by using a loss function, and then the gradient is uploaded to a server after passing through a local differential privacy model; the server collects gradients of a plurality of clients, and further updates model parameters to train a social recommendation model; and social recommendation is performed through local client embedding output by the social recommendation model. Data storage is dispersed, local user privacy data of the client is comprehensively fused, and social recommendation is cooperatively trained by using the server, so that social recommendation can be effectively realized, and the privacy of the data is protected.

Description

technical field [0001] The invention belongs to the technical field of data processing, and in particular relates to a social recommendation method based on a heterogeneous graph neural network. Background technique [0002] With the rapid development of the Internet and information computing, a large amount of data has been derived, and we have entered an era of information explosion. Massive information is generated every moment, and users are looking for useful information from a large amount of information. It is getting more and more difficult. Everyone has different interests, so a recommendation system that can realize thousands of people and faces has emerged as the times require and has become a current hot spot. The recommendation system recommends appropriate information to the user by exploring the behavior of the user to meet the individual needs of the user. It is designed to predict users' potential interest in items by learning embeddings. Additionally, re...

AI Technical Summary

Problems solved by technology

However, existing methods that use graph neural networks to simultaneously aggregate social connections and user-item interactions all require centralized storage of users’ social connections and item interactions, which leads to privacy issues.

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