Recommendation method of spatial adaptive graph convolutional network

Abstract

In recent years, a recommendation method based on a graph neural network achieves great success in academic and industrial circles, some research scholars simulate the social influence of recursive propagation in a social network through a high-order relationship among graph convolutional neural network modeling users, and feature vectors of high-order neighbors are utilized to constrain feature vectors of target users. In order to improve the accuracy of social recommendation, the influence propagation of the collaborative similarity between the user and the article hidden in the user and article interaction network is further captured, and the preference of the user changes along with the propagation of the social influence and the collaborative similarity influence. In combination with different characteristics of information representation of an actual recommendation scene, a user social domain and a user article interaction domain, the invention adaptively initializes a user potential feature vector in different semantic spaces to reflect the characteristic that a social relationship between users and an interaction relationship between user articles generate different influences on constraint user feature vectors. In addition, in order to enable the model to be more suitable for practical application, the invention discloses a rapid non-sampling optimizer to learn model parameters, and the model optimization efficiency is improved.

Description

[0001] technology space [0002] The invention relates to the technical field of machine learning and recommendation, in particular to a method for item recommendation using a space-adaptive graph convolutional network model. Background technique [0003] With the continuous expansion of users and network scale, Internet information has shown explosive growth, which has brought about the problem of information overload, which in turn makes people unable to timely and accurately mine target information in the face of massive data. As an important means of information filtering, the recommendation system is currently one of the most effective methods to solve the problem of information. Among them, the widely used recommendation technology is based on Collaborative Filtering (Collaborative Filtering, CF), its task is to mine the user's preference through the user-item historical interaction (for example, click, purchase, check-in, etc.) information, and finally recommend to the ...

AI Technical Summary

Problems solved by technology

However, the inventors found that there are two key issues that have not been fully investigated in previous studies:

[0005] (1) They do not consider the impact of different characteristics of social domain and user-item domain information on user feature learning. They usually initialize user latent feature representation in the same semantic space, and do not further capture the relationship between users and items in the user-item interaction network. Cooperative Similarity Propagation as a Complementary to User Preference Learning

[0006] (2) They usually rely on negative sampling to optimize the recommendation model, which makes the model training highly sensitive to the quality of the negative sampler, and it will be difficult to fully utilize the computing power of the graphics processing unit (GPU)

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