Recommendation method based on graph convolutional network and attention mechanism

Abstract

The invention discloses a recommendation method based on a graph convolutional network and an attention mechanism, and the method comprises the steps: constructing a user-article high-order connectivity interaction graph according to the interaction data of a user and an article; modeling according to high-order connection information of the high-order connectivity interaction graph, and generating a recommendation model; training a recommendation model according to the user and article interaction data; and after the training is completed, the recommendation model obtains an association score between the user and the article through the inner product, and finally, whether the article is recommended to the user or not is judged according to the association score. According to the method, embedded representation is learned in a user-article interaction bigraph, interaction information between a user and an article is considered in a model embedding layer, and high-order connectivity is modeled on a user-article interaction graph through embedding propagation, so that the model can learn high-dimensional feature information, more effective embedding is obtained, and the user-article interaction efficiency is improved. And the recommendation accuracy is improved.

Description

technical field [0001] The invention relates to the technical field of computer intelligence, in particular to a recommendation method based on a graph convolution network and an attention mechanism. Background technique [0002] With the emergence of huge data that has grown exponentially in the Internet in recent years, our society has ushered in the era of "big data". In the era of "big data", we will inevitably encounter difficulties in information processing and selection in the face of such a huge amount of data. At this time, the recommendation system acts as an intelligent information filter to help us find information from big data. The content, goods, services, etc. we need are then presented to us. At present, recommendation algorithms play a vital role in e-commerce, social media, advertising and other fields. It usually mines feature information through item features, user preferences, and interaction history information between items and users. [0003] Coll...

AI Technical Summary

Problems solved by technology

However, its bottleneck is still focused on the ability of the current model to extract features to be strengthened, and it still faces problems such as data sparsity, and the existing model does not aggregate the different influences of neighboring nodes for effective modeling

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