利用图神经网络的互补产品推荐OA北大核心CSTPCD

Complementary product recommendations can provide complementary matching products for the convenience of users.However,existing work using graph neural networks ignores the multimodal information of products,and the performance of multimodal models is affected when the modal information is missing.In addition,existing multimodal models simply concatenate the modalities,ignoring the connections between the modalities.For these reasons,a comple-mentary product recommendation using graph neural network(CPRUG)model is proposed.The model combines the graph neural network with multimodal information to strengthen the product representation.Then,it uses the graph atten-tion network to deal with the multimodal absence problem,maintain the performance of the model,and improve the robustness of the model.Finally,it uses the common attention mechanism and matrix factorized bilinear pooling method to fuse multimodal features and learn the complementary relationship of products.Experiments are conducted on the Amazon dataset,and the experimental results show that the model outperforms other baseline models.