JBM-Diff applies conditional graph diffusion to remove preference-irrelevant multimodal noise and false-positive/negative behaviors, then augments training data via partial-order credibility scoring.
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MoS applies theme-aware routing to extract multi-scale theme-specific subsequences from noisy long user sequences, achieving state-of-the-art recommendation performance with fewer FLOPs than comparable MoE models.
GDDM is a diffusion-based purification method with a graph structure refiner and node feature regularizer that defends against multiple adversarial attack types on graphs.
DIAURec unifies intent and language modeling to reconstruct and optimize representations in prototype and distribution spaces, outperforming baselines on three datasets.
DSCF is a deep social collaborative filtering model that uses distant neighbors and item-relevant opinions from social networks to improve recommendation accuracy over prior deep models.
citing papers explorer
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Joint Behavior-guided and Modality-coherence Conditional Graph Diffusion Denoising for Multi Modal Recommendation
JBM-Diff applies conditional graph diffusion to remove preference-irrelevant multimodal noise and false-positive/negative behaviors, then augments training data via partial-order credibility scoring.
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Mixture of Sequence: Theme-Aware Mixture-of-Experts for Long-Sequence Recommendation
MoS applies theme-aware routing to extract multi-scale theme-specific subsequences from noisy long user sequences, achieving state-of-the-art recommendation performance with fewer FLOPs than comparable MoE models.
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Graph Defense Diffusion Model
GDDM is a diffusion-based purification method with a graph structure refiner and node feature regularizer that defends against multiple adversarial attack types on graphs.
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DIAURec: Dual-Intent Space Representation Optimization for Recommendation
DIAURec unifies intent and language modeling to reconstruct and optimize representations in prototype and distribution spaces, outperforming baselines on three datasets.
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Deep Social Collaborative Filtering
DSCF is a deep social collaborative filtering model that uses distant neighbors and item-relevant opinions from social networks to improve recommendation accuracy over prior deep models.
- Universal Graph Backdoor Defense: A Feature-based Homophily Perspective