Gated Multimodal Graph Learning for Personalized Recommendation

• URL: http://arxiv.org/abs/2506.00107v1
• Date: Fri, 30 May 2025 16:57:17 GMT
• Title: Gated Multimodal Graph Learning for Personalized Recommendation
• Authors: Sibei Liu, Yuanzhe Zhang, Xiang Li, Yunbo Liu, Chengwei Feng, Hao Yang,
• Abstract summary: Multimodal recommendation has emerged as a promising solution to alleviate the cold-start and sparsity problems in collaborative filtering.<n>We propose RLMultimodalRec, a lightweight and modular recommendation framework that combines graph-based user modeling with adaptive multimodal item encoding.
• Score: 9.466822984141086
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal recommendation has emerged as a promising solution to alleviate the cold-start and sparsity problems in collaborative filtering by incorporating rich content information, such as product images and textual descriptions. However, effectively integrating heterogeneous modalities into a unified recommendation framework remains a challenge. Existing approaches often rely on fixed fusion strategies or complex architectures , which may fail to adapt to modality quality variance or introduce unnecessary computational overhead. In this work, we propose RLMultimodalRec, a lightweight and modular recommendation framework that combines graph-based user modeling with adaptive multimodal item encoding. The model employs a gated fusion module to dynamically balance the contribution of visual and textual modalities, enabling fine-grained and content-aware item representations. Meanwhile, a two-layer LightGCN encoder captures high-order collaborative signals by propagating embeddings over the user-item interaction graph without relying on nonlinear transformations. We evaluate our model on a real-world dataset from the Amazon product domain. Experimental results demonstrate that RLMultimodalRec consistently outperforms several competitive baselines, including collaborative filtering, visual-aware, and multimodal GNN-based methods. The proposed approach achieves significant improvements in top-K recommendation metrics while maintaining scalability and interpretability, making it suitable for practical deployment.

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