GTC improves multi-modal recommendation by using user-conditional diffusion-based feature filtering and total correlation optimization, achieving up to 28.3% gains in NDCG@5 on benchmarks.
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Behavior-guided calibration converts co-user overlap into signed evidence applied only to multimodal recommender shortlists and yields consistent gains on Amazon Baby, Sports, and Electronics datasets.
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User-Aware Conditional Generative Total Correlation Learning for Multi-Modal Recommendation
GTC improves multi-modal recommendation by using user-conditional diffusion-based feature filtering and total correlation optimization, achieving up to 28.3% gains in NDCG@5 on benchmarks.
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Behavior-Guided Candidate Calibration for Multimodal Recommendation
Behavior-guided calibration converts co-user overlap into signed evidence applied only to multimodal recommender shortlists and yields consistent gains on Amazon Baby, Sports, and Electronics datasets.