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

arXiv:2609.02152 · cs.IR, cs.AI · Submitted 2026-09-02 · Read on arXiv

cs.IR, cs.AI

Submitted: 2026-09-02

Updated: 2026-09-02

Code: https://github.com/Camilla-jl/Orthorec

Terminology

• Alignment — 23× in this paper · explained in 1 episode
• Contrastive Learning — 14× in this paper · explained in 5 episodes
• Robustness — 9× in this paper · explained in 5 episodes
• Sparsity — 9× in this paper · explained in 4 episodes
• Embeddings — 8× in this paper · explained in 2 episodes
• Hypergraph — 7× in this paper · explained in 2 episodes
• Latent Space — 7× in this paper · explained in 7 episodes
• Ablation — 4× in this paper · explained in 1 episode
• Collaborative-Guided Orthogonal Purification — 3× in this paper
• Core — 3× in this paper · explained in 2 episodes
• Semantic Alignment — 3× in this paper · explained in 3 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Accuracy — 2× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 2 episodes
• Aggregation — 2× in this paper · explained in 1 episode
• Attention Mechanism — 2× in this paper · explained in 2 episodes
• Bayesian Personalized Ranking — 2× in this paper
• Benchmark — 2× in this paper · explained in 2 episodes
• Decoupling — 2× in this paper · explained in 2 episodes
• GNNs — 2× in this paper · explained in 2 episodes
• Latent Representation — 2× in this paper · explained in 2 episodes
• Representation Learning — 2× in this paper · explained in 2 episodes
• Self-Supervised Learning — 2× in this paper · explained in 7 episodes
• Adaptive Graph — 1× in this paper · explained in 1 episode
• Causal Inference — 1× in this paper · explained in 4 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Graph Neural Networks (GNNs) — 1× in this paper · explained in 7 episodes
• Knowledge Distillation — 1× in this paper · explained in 11 episodes
• Latent Representations — 1× in this paper · explained in 3 episodes
• Modality Gap — 1× in this paper · explained in 4 episodes
• Multimodal Learning — 1× in this paper · explained in 2 episodes
• Objective Function — 1× in this paper · explained in 2 episodes
• Over-smoothing — 1× in this paper · explained in 4 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Tokenization — 1× in this paper · explained in 2 episodes

Sources

• M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation
• BPR: Bayesian Personalized Ranking from Implicit Feedback
• Contrastive Learning with Hard Negative Samples
• DropEdge: Towards Deep Graph Convolutional Networks on Node Classification
• Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

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