利用图神经网络的互补产品推荐

doi:10.3778/j.issn.1002-8331.2304-0222

摘要/Abstract

摘要： 互补产品推荐可以提供互补搭配的产品，为用户提供便利。然而现有使用图神经网络的工作忽视了产品的多模态信息，以及多模态模型在模态信息缺失时性能会受到影响。现有多模态模型只是将模态简单拼接，忽略了模态间的联系。因此，提出了一种利用图神经网络的互补产品推荐模型（complementary product recommendation using graph neural network，CPRUG）。该模型将图神经网络与多模态信息结合，强化产品的表征；利用图注意力网络，应对多模态缺失问题，维持模型的性能，提高模型的鲁棒性；使用共同注意力机制和矩阵分解双线性池化方法来融合多模态特征，学习产品的互补关系。在Amazon数据集上进行了实验，实验结果表明，模型的性能优于其他基线模型。

关键词: 互补产品, 图神经网络, 推荐系统, 多模态

Abstract: 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 complementary 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 attention 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.

Key words: complementary product, graph neural network, recommendation system, multimodal

倪伟竣, 纪淑娟, 梁永全. 利用图神经网络的互补产品推荐[J]. 计算机工程与应用, 2024, 60(10): 292-300.

NI Weijun, JI Shujuan, LIANG Yongquan. Complementary Product Recommendation Using Graph Neural Network[J]. Computer Engineering and Applications, 2024, 60(10): 292-300.

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