Grade: Generative graph contrastive learning for multimodal recommendation

Multimodal recommender systems based on graph convolutional networks have made significant progress by integrating multiple modal data for item recommendation. While most existing approaches learn user and item representations through modality-related interaction graphs, these approaches still encou...

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Vydáno v:Neurocomputing (Amsterdam) Ročník 657; s. 131630
Hlavní autoři: Ping, Yu-Chao, Wang, Shu-Qin, Yang, Zi-Yi, Dong, Yong-Quan, Hu, Meng-Xiang, Zhang, Pei-Lin
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 07.12.2025
Témata:
Contrastive learning
Graph convolutional network
Multimodal recommendation systems
Variational graph autoencoder
Multimodal recommendation systems
Graph convolutional network
Variational graph autoencoder
Contrastive learning
ISSN:0925-2312
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Shrnutí:Multimodal recommender systems based on graph convolutional networks have made significant progress by integrating multiple modal data for item recommendation. While most existing approaches learn user and item representations through modality-related interaction graphs, these approaches still encounter challenges inherent to graph convolutional networks: over-smoothing. To address this challenge, we propose a model named Grade, Generative Graph Contrastive Learning for Multimodal Recommendations. It combines generative models and contrastive learning and design four task losses. In particular, the generative graph contrastive task generates contrastive views inter-modal through variational graph reconstruction, effectively aligning modal features to improve user and item representations. In addition, the feature perturbation contrastive task generates multimodal noisy views with interference for intra-modal contrast through noise-based self-supervised learning, effectively enhancing the robustness of modality-specific representations. Finally, we incorporate the Variational Graph Autoencoders (VGAE) task and the Bayesian Personalized Ranking (BPR) task. The combination of these four task losses effectively mitigates the issues of over-smoothing. Extensive experiments conducted on three publicly available datasets confirm the superiority of our model. The related code is available on https://github.com/Ricardo-Ping/Grade.
ISSN:0925-2312
DOI:10.1016/j.neucom.2025.131630