Modality-Guided Mixture of Graph Experts with Entropy-Triggered Routing for Multimodal Recommendation
arXiv:2602.20723v1 Announce Type: new 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 c
arXiv:2602.20723v1 Announce Type: new 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 augments the interaction graph with content-induced edges, improving coverage for sparse and long-tail items while preserving collaborative structure via parallel encoding and lightweight fusion. At the fusion level, MAGNET employs structured experts with explicit modality roles -- dominant, balanced, and complementary -- enabling a more interpretable and adaptive combination of behavioral, visual, and textual cues. To further stabilize sparse routing and prevent expert collapse, we introduce a two-stage entropy-weighting mechanism that monitors routing entropy. This mechanism automatically transitions training from an early coverage-oriented regime to a later specialization-oriented regime, progressively balancing expert utilization and routing confidence. Extensive experiments on public benchmarks demonstrate consistent improvements over strong baselines.
Executive Summary
This article proposes MAGNET, a novel multimodal recommendation framework that tackles the challenges of multimodal signal fusion, modality imbalance, and entangled representations. By introducing modality-guided routing, structure-aware graph augmentation, and dual-view graph learning, MAGNET enhances controllability, stability, and interpretability in multimodal fusion. The framework's adaptability and interpretability are further improved through the use of structured experts and an entropy-triggered routing mechanism. Experimental results demonstrate consistent improvements over strong baselines, showcasing MAGNET's potential in real-world applications.
Key Points
- ▸ Modality-guided mixture of graph experts for multimodal recommendation
- ▸ Entropy-triggered routing for adaptive fusion
- ▸ Structure-aware graph augmentation for improved coverage and collaborative structure
- ▸ Dual-view graph learning for parallel encoding and lightweight fusion
- ▸ Structured experts for explicit modality roles and interpretable fusion
Merits
Strength in addressing modality imbalance
MAGNET's modality-guided routing and structure-aware graph augmentation effectively address the issue of modality imbalance, enabling more accurate and interpretable fusion of multimodal signals.
Improved controllability and stability
The framework's entropy-triggered routing mechanism and dual-view graph learning module enhance controllability and stability in multimodal fusion, reducing the risk of expert collapse and improving overall performance.
Enhanced interpretability
MAGNET's use of structured experts and explicit modality roles provides valuable insights into the fusion process, enabling more informed decision-making and improved model interpretability.
Demerits
Potential complexity and computational overhead
The framework's multiple components and mechanisms may introduce complexity and computational overhead, particularly for large-scale datasets and real-time applications.
Limited evaluation on diverse datasets
The article's experimental evaluation is primarily based on a single benchmark dataset, which may not fully represent the diversity of real-world scenarios and applications.
Expert Commentary
While MAGNET demonstrates promising results in addressing the challenges of multimodal signal fusion, its potential complexity and computational overhead are notable concerns. Moreover, the limited evaluation on diverse datasets highlights the need for further experimentation to validate the framework's performance in various real-world scenarios. Nevertheless, MAGNET's adaptability, interpretability, and controllability make it a valuable contribution to the field of recommendation systems, particularly in the context of multimodal data.
Recommendations
- ✓ Further experimentation on diverse datasets to validate the framework's performance and scalability
- ✓ Investigation of potential applications in real-world recommendation systems, such as e-commerce and social media