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

• URL: http://arxiv.org/abs/2609.02152v1
• Date: Wed, 02 Sep 2026 06:06:30 GMT
• Title: Beyond Modality Harmony: Orthogonal Purification and Topology-Guided MoE for Conflict-Aware Multimodal Recommendation
• Abstract summary: We propose Orthogonal purification and topology-guided MoE for conflict-aware multimodal Recommendation (OrthoRec)<n>At its core, OrthoRec introduces Collaborative-Guided Orthogonal Purification (CGOP)<n>We show that OrthoRec consistently outperforms competitive recent baselines and exhibits improved robustness under modality noise and item sparsity.
• Score: 6.78317230214304
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimodal Recommender Systems (MRSs) typically rely on a flawed "modality harmony" assumption, presuming that multimodal features are inherently beneficial and strictly aligned with users' collaborative interaction patterns. However, modality-topology conflicts are ubiquitous in real-world scenarios due to deceptive visual clickbaits and mismatched semantics. Blindly integrating these noisy modalities inevitably pollutes the pristine collaborative space, causing severe representation distortion. To address this, we propose Orthogonal purification and topology-guided MoE for conflict-aware multimodal Recommendation (OrthoRec). At its core, OrthoRec introduces Collaborative-Guided Orthogonal Purification (CGOP), which geometrically decouples multimodal features into directions parallel and orthogonal to a pure collaborative anchor. By adaptively truncating the orthogonal noise with an energy-preserving normalization, CGOP rectifies deceptive semantic directions while preserving the modality's intrinsic representation capacity. Furthermore, we design a Topology-Aware Routing Mixture-of-Experts (TAR-MoE). Guided by the collaborative topology, TAR-MoE employs decoupled sigmoid gating to break the zero-sum bottleneck of traditional softmax attention, autonomously determining the injection scale for each purified modality. Finally, a safe-SSL objective is introduced to dynamically penalize the forced contrastive alignment of contradictory pairs. Experiments on three real-world Amazon datasets show that OrthoRec consistently outperforms competitive recent baselines and exhibits improved robustness under modality noise and item sparsity.

Related papers

• MURAL: Multimodal Uncertainty-aware Recommendation via Adaptive edge Learning [8.921188716837312]
  MURAL is a unified framework that shifts multimodal recommendation from fixed structural augmentation to dynamic topology discovery.<n>To address structural rigidity, an Adaptive Edge Learner combines a differentiable retrieval-augmented strategy with an approximate nearest neighbor search to discover latent item-item correlations.<n>To address semantic fragility, an Uncertainty-Aware Fusion module models the aleatoric uncertainty of heterogeneous modalities, dynamically down-weighting unreliable features.
  arXiv  Detail & Related papers  (2026-09-04T00:06:53Z)
• N-GRPO: Embedding-Level Neighbor Mixing for Enhanced Policy Optimization [55.14402862283128]
  We introduce N-GRPO, a novel exploration strategy integrated into the Group Relative Policy Optimization framework.<n>Rather than relying on token-level sampling or native embedding-level noise, our approach leverages Semantic Neighbor Mixing.<n>N-GRPO achieves consistent improvements over strong baselines on math reasoning benchmarks and also exhibits robust generalization capabilities on out-of-distribution tasks.
  arXiv  Detail & Related papers  (2026-06-09T12:21:27Z)
• Intra-Modal Neighbors Never Lie: Rectifying Inter-Modal Noisy Correspondence via Graph-Based Intra-Modal Reasoning [35.81581893839085]
  Large-scale web-harvested datasets have fueled the progress of cross-modal retrieval but inevitably suffer from noisy correspondence.<n>We propose a novel framework, Intra-modal Neighbor-aware Noise Rectification (IN2R), which shifts the paradigm from searching for a substitute to synthesizing a reliable supervision target.
  arXiv  Detail & Related papers  (2026-06-02T12:26:28Z)
• Better with Less: Tackling Heterogeneous Multi-Modal Image Joint Pretraining via Conditioned and Degraded Masked Autoencoder [34.73963627819185]
  High-resolution (HR) joint optical and synthetic aperture radar (SAR) pretraining seeks modality synergy to mutually enhance single-source representations.<n>We propose CoDe-MAE, pioneering a textitbetter synergy with less alignment philosophy.<n>CoDe-MAE successfully prevents representation degradation and establishes new state-of-the-art performance across diverse single- and bi-modal downstream tasks.
  arXiv  Detail & Related papers  (2026-04-18T10:23:00Z)
• Chain of Modality: From Static Fusion to Dynamic Orchestration in Omni-MLLMs [84.3271821505699]
  Chain of Modality (CoM) is an agentic framework that transitions multimodal fusion from passive concatenation to dynamic orchestration.<n> operating in either a training-free or a data-efficient SFT setting, CoM achieves robust and consistent generalization across diverse benchmarks.
  arXiv  Detail & Related papers  (2026-04-16T01:21:14Z)
• OmniVL-Guard: Towards Unified Vision-Language Forgery Detection and Grounding via Balanced RL [63.388513841293616]
  Existing forgery detection methods fail to handle the interleaved text, images, and videos prevalent in real-world misinformation.<n>To bridge this gap, this paper targets to develop a unified framework for omnibus vision-language forgery detection and grounding.<n>We propose textbf OmniVL-Guard, a balanced reinforcement learning framework for omnibus vision-language forgery detection and grounding.
  arXiv  Detail & Related papers  (2026-02-11T09:41:36Z)
• UniRoute: Unified Routing Mixture-of-Experts for Modality-Adaptive Remote Sensing Change Detection [6.323154336421137]
  UniRoute is a unified framework for modality-adaptive learning.<n>We introduce an Adaptive Receptive Field Routing MoE module to disentangle local spatial details from global semantic context.<n>We also propose a Consistency-Aware Self-Distillation strategy that stabilizes unified training under data-scarce heterogeneous settings.
  arXiv  Detail & Related papers  (2026-01-21T09:21:25Z)
• Test-time Adaptive Hierarchical Co-enhanced Denoising Network for Reliable Multimodal Classification [55.56234913868664]
  We propose Test-time Adaptive Hierarchical Co-enhanced Denoising Network (TAHCD) for reliable learning on multimodal data.<n>The proposed method achieves superior classification performance, robustness, and generalization compared with state-of-the-art reliable multimodal learning approaches.
  arXiv  Detail & Related papers  (2026-01-12T03:14:12Z)
• Calibrated Multimodal Representation Learning with Missing Modalities [100.55774771852468]
  Multimodal representation learning harmonizes distinct modalities by aligning them into a unified latent space.<n>Recent research generalizes traditional cross-modal alignment to produce enhanced multimodal synergy but requires all modalities to be present for a common instance.<n>We provide theoretical insights into this issue from an anchor shift perspective.<n>We propose CalMRL for multimodal representation learning to calibrate incomplete alignments caused by missing modalities.
  arXiv  Detail & Related papers  (2025-11-15T05:01:43Z)
• UniMRSeg: Unified Modality-Relax Segmentation via Hierarchical Self-Supervised Compensation [104.59740403500132]
  Multi-modal image segmentation faces real-world deployment challenges from incomplete/corrupted modalities degrading performance.<n>We propose a unified modality-relax segmentation network (UniMRSeg) through hierarchical self-supervised compensation (HSSC)<n>Our approach hierarchically bridges representation gaps between complete and incomplete modalities across input, feature and output levels.
  arXiv  Detail & Related papers  (2025-09-19T17:29:25Z)
• Dynamic Weighted Combiner for Mixed-Modal Image Retrieval [8.683144453481328]
  Mixed-Modal Image Retrieval (MMIR) as a flexible search paradigm has attracted wide attention. Previous approaches always achieve limited performance, due to two critical factors. We propose a Dynamic Weighted Combiner (DWC) to tackle the above challenges.
  arXiv  Detail & Related papers  (2023-12-11T07:36:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.