Can Complementary Signals Bridge Similarity Islands? Manifold-Augmented Graph Embedding for Multimodal Recommendation

Multimodal Recommender Systems (MMRS) leverage rich item features to mitigate data sparsity, yet their success largely hinges on the foundational homophily assumption. This paradigm makes existing models adept at recommending similar substitutes but poorly equipped to handle heterophilic complementarity. Consequently, complementary items often become geometrically isolated, leading to fragmented “Similarity Islands” and semantic drift when standard propagation is applied. To overcome these limitations, we propose MAGE (Manifold-Augmented Graph Embedding), a novel framework that decouples complementarity modeling into offline geometric warping and online topological propagation. The offline phase enhances item representations by extracting complement-aware residual directions via a Soft Conceptor mechanism and injecting them under a local gap-preserving constraint, enabling the model to capture functional relationships while reducing the risk of semantic drift. Synergistically, the online phase employs an asymmetric dual-stream GNN that treats visual features as stable anchors and augmented textual features as flexible probes, fused through a self-calibrated cross-modal mechanism to reconcile shared semantics and modality-specific residual cues. Extensive experiments on four Amazon datasets show that MAGE yields consistent improvements over strong multimodal recommendation baselines. Together with the supporting analyses in the appendix, these results suggest that geometry-aware augmentation can alleviate the semantic mismatch that arises in complementary recommendation settings. Our code is available at https://anonymous.4open.science/r/mage-4C60.