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arXiv AI By Jialin Liu, Zhaorui Zhang, Ray C. C. Cheung

Beyond Modality Harmony: Orthogonal Purification and Topology-Guided MoE for Conflict-Aware Multimodal Recommendation

Read the original on arXiv AI →

The paper introduces OrthoRec, a multimodal recommender system that challenges the common "modality harmony" assumption by addressing conflicts between multimodal features and collaborative patterns. It employs Collaborative‑Guided Orthogonal Purification (CGOP) to separate useful multimodal signals from noisy orthogonal components, and a Topology‑Aware Routing Mixture‑of‑Experts (TAR‑MoE) to adaptively integrate purified modalities based on collaborative topology. Experiments on Amazon datasets demonstrate that OrthoRec outperforms recent baselines and remains robust to modality noise and item sparsity.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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