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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Neurocomputing (Amsterdam) Jg. 657; S. 131630
Hauptverfasser: Ping, Yu-Chao, Wang, Shu-Qin, Yang, Zi-Yi, Dong, Yong-Quan, Hu, Meng-Xiang, Zhang, Pei-Lin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 07.12.2025
Schlagworte:
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
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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