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[2602.20723] Modality-Guided Mixture of Graph Experts with Entropy-Triggered Routing for Multimodal Recommendation
Summary
The paper presents MAGNET, a novel multimodal recommendation framework that utilizes a mixture of adaptive graph experts and entropy-triggered routing to enhance recommendation quality and interpretability.
Why It Matters
As recommendation systems increasingly rely on diverse data sources, effective integration of multimodal signals is essential. MAGNET addresses common challenges such as modality imbalance and sparse feedback, making it a significant contribution to the field of artificial intelligence and recommendation systems.
Key Takeaways
- MAGNET improves multimodal recommendation by controlling what and how to fuse different data types.
- The framework enhances interpretability with structured experts assigned specific modality roles.
- A two-stage entropy-weighting mechanism stabilizes routing and prevents expert collapse during training.
Computer Science > Artificial Intelligence arXiv:2602.20723 (cs) [Submitted on 24 Feb 2026] Title:Modality-Guided Mixture of Graph Experts with Entropy-Triggered Routing for Multimodal Recommendation Authors:Ji Dai, Quan Fang, Dengsheng Cai View a PDF of the paper titled Modality-Guided Mixture of Graph Experts with Entropy-Triggered Routing for Multimodal Recommendation, by Ji Dai and Quan Fang and Dengsheng Cai View PDF HTML (experimental) Abstract:Multimodal recommendation enhances ranking by integrating user-item interactions with item content, which is particularly effective under sparse feedback and long-tail distributions. However, multimodal signals are inherently heterogeneous and can conflict in specific contexts, making effective fusion both crucial and challenging. Existing approaches often rely on shared fusion pathways, leading to entangled representations and modality imbalance. To address these issues, we propose \textbf{MAGNET}, a \textbf{M}odality-Guided Mixture of \textbf{A}daptive \textbf{G}raph Experts \textbf{N}etwork with Progressive \textbf{E}ntropy-\textbf{T}riggered Routing for Multimodal Recommendation, designed to enhance controllability, stability, and interpretability in multimodal fusion. MAGNET couples interaction-conditioned expert routing with structure-aware graph augmentation, so that both \emph{what} to fuse and \emph{how} to fuse are explicitly controlled and interpretable. At the representation level, a dual-view graph learning module...
