GRE-MC retrieves relevant subgraphs and uses a graph transformer plus sparse codebook to complete missing modalities, outperforming prior methods on recommendation benchmarks.
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cs.IR 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
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.
SG-URInit builds semantically enriched initial user representations for multimodal recommenders by fusing local item modality features with global cluster semantics, closing the gap with item representations without extra training.
citing papers explorer
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Robust Multimodal Recommendation via Graph Retrieval-Enhanced Modality Completion
GRE-MC retrieves relevant subgraphs and uses a graph transformer plus sparse codebook to complete missing modalities, outperforming prior methods on recommendation benchmarks.
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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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Well Begun is Half Done: Training-Free and Model-Agnostic Semantically Guaranteed User Representation Initialization for Multimodal Recommendation
SG-URInit builds semantically enriched initial user representations for multimodal recommenders by fusing local item modality features with global cluster semantics, closing the gap with item representations without extra training.