DDRec: Dual Denoising Multimodal Graph Recommendation

Abstract

Multimodal recommendation systems have made significant progress by leveraging graph convolutional networks to integrate user behavior with item content, including images and text. However, these systems still encounter two major challenges: noise edges in interaction graphs and noise in multimodal features of items. Existing works tend to address only one type of noise problem to enhance recommendation performance. This article proposes a new Dual Denoising Multimodal Graph Recommendation (DDRec) model, designed to enhance multimodal recommendation systems by tackling both challenges simultaneously. Specifically, we design two denoising techniques: hard denoising and soft denoising. For noise edges in interaction graphs, the hard denoising method uses preference scores of user nodes and item nodes in different modality interaction graphs as edge weights and prunes edges below a certain threshold to eliminate noise. For noise in multimodal features, the soft denoising method leverages item and item semantic graph information to denoise modal features, thus obtaining modality features related to user preferences. Finally, we employ contrastive learning to compare user and item representations derived from the denoised modality interaction graphs against those from the original graph, ensuring the consistency of nodes across various views. Our comprehensive experiments across four public datasets validate the enhanced performance and effectiveness of the DDRec model.