Attribute reasoning and product recommendation method based on adaptive graph convolutional network

Abstract

The invention discloses an attribute reasoning and product recommendation method based on an adaptive graph convolutional network. The method comprises the following steps: 1, constructing heterogeneous data: a scoring matrix of a user to a product, a user attribute matrix, a product attribute matrix, a user attribute index matrix and a product attribute index matrix; 2, performing missing value filling preprocessing on the user attribute matrix and the product attribute matrix; 3, obtaining a cooperative matrix through one-hot coding; 4, constructing a feature fusion layer according to the attribute matrix and the collaborative matrix; 5, carrying out feature propagation through a graph convolution layer; 6, constructing a prediction layer to perform attribute reasoning and product recommendation; 7, updating the node attribute matrix according to the output result of the prediction layer; and 8, repeating the steps 4-7 until the attribute reasoning and product recommendation effectsare optimal. According to the method, the high-order structure information of the graph, the internal interaction between the node attributes and the potential association between the node attributesand the link relationship can be fully mined, so that more accurate attribute reasoning and product recommendation are realized.

Description

technical field [0001] The present invention relates to the field of attribute reasoning and product recommendation, in particular to an attribute reasoning and product recommendation method based on an adaptive graph convolutional network. Background technique [0002] The recommendation system effectively alleviates the problem of information overload, and has been successfully applied to platforms such as e-commerce, music, movies, and social networking. The model based on collaborative filtering is one of the most mainstream recommendation systems, which mines the user's historical records to make personalized product recommendations. The recommendation system based on collaborative filtering is widely used, but its performance is often limited by the sparsity of user behavior data. [0003] Attribute-enhanced collaborative filtering system uses rich attribute features of users (gender, age, occupation, etc.) and products (price, quality, category) to model users and pr...

AI Technical Summary

Problems solved by technology

Existing attribute reasoning works use semi-supervised graph learning (label propagation, graph regularization, deep models, etc.) to predict missing attributes, but the accuracy of attribute reasoning needs to be improved

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