Item recommendation method and apparatus, model training method and apparatus, device, computer readable storage medium and computer program product

Abstract

This application provides a product recommendation method, model training method, apparatus, device, computer-readable storage medium, and computer program product, relating to the field of artificial intelligence. The method includes: acquiring a bipartite graph corresponding to at least one user and at least one product, and performing convolution processing on the bipartite graph based on an attention-based relational graph convolutional network to obtain a representation set; determining the user-product pair distance corresponding to the user-product pair representation, and determining the structural features corresponding to the user-product pair representation based on the user-product pair distance and the bipartite graph; generating a prediction information set based on each user-product pair representation and each structural feature; and recommending products to the user based on the prediction information set. This disclosed method can accurately recommend products to users.