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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TESMR progressively enhances multimodal recipe features through content-based, relation-based, and learning-based stages, achieving 7-15% higher Recall@10 than baselines on two real-world datasets.
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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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From Raw Features to Effective Embeddings: A Three-Stage Approach for Multimodal Recipe Recommendation
TESMR progressively enhances multimodal recipe features through content-based, relation-based, and learning-based stages, achieving 7-15% higher Recall@10 than baselines on two real-world datasets.