Graph embedding improves multimodal recommendation by linking complementary items
Can Complementary Signals Bridge Similarity Islands? Manifold-Augmented Graph Embedding for Multimodal Recommendation
Information Retrieval
Summary
Recommender systems often struggle to connect items that work well together but aren’t very similar, like complementary products. The authors show that standard methods can isolate these items into separate groups, making recommendations less helpful. They propose a new approach called MAGE that changes how item similarities are represented and combined, allowing the system to better recognize both similar and complementary items. Their experiments on Amazon shopping data show that this method improves recommendation accuracy.
What this means in practice
- •For ecommerce platform engineers: Enhance product recommendation engines to better suggest functionally related complementary items alongside similar substitutes.$Commercial implications: Enables ecommerce platforms to sell more complementary products through improved personalized recommendations, increasing revenue.
- •For digital marketing teams: Improve targeting strategies by identifying complementary product clusters beyond obvious similarities in customer purchase behavior.
Authors
Zhenye Yang, Jinpeng Chen, Wenbo Fu, Jun Ma, Huan Li, Qimin Zhou, Tao Wang, Li Sun, Senzhang Wang
Abstract
Multimodal Recommender Systems (MMRS) leverage rich item features to mitigate data sparsity, yet similarity-driven graph construction and propagation can favor semantically similar substitutes over functionally related complements. When behavioral associations connect semantically distant items, complementary signals can remain geometrically isolated, yielding fragmented "Similarity Islands" and semantic drift when standard propagation is applied. To overcome these limitations, we propose MAGE (Manifold-Augmented Graph Embedding), a framework for personalized multimodal ranking 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, incorporating co-purchase evidence while controlling the magnitude of geometric perturbation. 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://github.com/Alex-bupt/mage.