AdaSID adaptively regulates semantic ID overlaps in multimodal recommendations to improve retrieval performance, codebook utilization, and downstream metrics like GMV.
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8 Pith papers cite this work. Polarity classification is still indexing.
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TimeMM proposes a time-as-operator spectral filtering framework with adaptive mixing and modality routing to model non-stationary multimodal user preferences in recommendation systems.
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.
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.
TextBridgeGNN pre-trains GNNs using text-guided hierarchical propagation to enable effective cross-domain knowledge transfer in recommendations.
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.
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.
TASTE dataset and MuQ-token aggregation enable effective use of audio features from large music models to improve content-based music recommendations over collaborative filtering alone.
citing papers explorer
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Beyond Static Collision Handling: Adaptive Semantic ID Learning for Multimodal Recommendation at Industrial Scale
AdaSID adaptively regulates semantic ID overlaps in multimodal recommendations to improve retrieval performance, codebook utilization, and downstream metrics like GMV.
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TimeMM: Time-as-Operator Spectral Filtering for Dynamic Multimodal Recommendation
TimeMM proposes a time-as-operator spectral filtering framework with adaptive mixing and modality routing to model non-stationary multimodal user preferences in recommendation systems.
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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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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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TextBridgeGNN: Pre-training Graph Neural Network for Cross-Domain Recommendation via Text-Guided Transfer
TextBridgeGNN pre-trains GNNs using text-guided hierarchical propagation to enable effective cross-domain knowledge transfer in recommendations.
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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.
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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.
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Revisiting Content-Based Music Recommendation: Efficient Feature Aggregation from Large-Scale Music Models
TASTE dataset and MuQ-token aggregation enable effective use of audio features from large music models to improve content-based music recommendations over collaborative filtering alone.