MM-DAGNN: Modality-Disentangled Adaptive Graph Neural Networks for Multimodal E-commerce Recommendation

We proposed MM-DAGNN, a Modality-Disentangled Adaptive Graph Neural Network designed to address key challenges in multimodal e-commerce recommendation systems. Specifically, MM-DAGNN tackles the limitations of traditional models that assume uniform modality propagation, static fusion, and entangled projections. The proposed model integrates (i) modality-specific receptive fields Kv , Kt, Kid, (ii) a per-item adaptive fusion gate, and (iii) a contrastive disentanglement regularizer to improve the handling of heterogeneous modalities, against seven competitive baselines on the public AliExpress dataset. We treat country aggregated implicit feedback as pseudo-users and apply 5-core filtering, yielding a 177 × 5,073 bipartite graph with 105,419 edges. Three findings stand out. (F1) On Recall@20 and on every secondary metric, MM-DAGNN is competitive. MMGCN (0.0238) and LATTICE (0.0236) achieve the best Recall@20, while MM-DAGNN is the strongest method on Recall@10, NDCG, and MAP@20, a pattern consistent with recent reproducibility findings in collaborative filtering. (F2) A 4×4 grid over (Kv , Kt) shows a near-isotropic accuracy surface: the Recall@20 maximum sits at the diagonal (Kv =1, Kt=1) and the NDCG@2016 maximum at (Kv =2, Kt=2), contradicting the recent claim that visual and textual modalities require systematically different propagation depths on this dataset. (F3) The adaptive gate produces a high Modality Balance Index (MBI = 0.70) without harming accuracy, indicating that the gate genuinely uses all three streams rather than collapsing onto one. By solving the problem of modality imbalance and improving the adaptation across different streams, we contribute to a more flexible and accurate approach to multimodal recommendation.