SID-MLP distills autoregressive generative recommenders into efficient position-specific MLP heads for Semantic ID tasks, achieving 8.74x faster inference with matching accuracy.
Beyond unimodal boundaries: Generative recom- mendation with multimodal semantics.arXiv preprint arXiv:2503.23333
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Autoregressive semantic ID generation creates tree-induced probability correlations that prevent generative recommenders from capturing simple patterns; Latte adds latent tokens to relax these correlations.
SynGR is a synergistic generative recommendation method that masks dominant-modal tokens and uses contrastive learning to capture cross-modal synergies, outperforming prior methods on Amazon Arts, Games, and Instruments.
citing papers explorer
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MLPs are Efficient Distilled Generative Recommenders
SID-MLP distills autoregressive generative recommenders into efficient position-specific MLP heads for Semantic ID tasks, achieving 8.74x faster inference with matching accuracy.
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Expressiveness Limits of Autoregressive Semantic ID Generation in Generative Recommendation
Autoregressive semantic ID generation creates tree-induced probability correlations that prevent generative recommenders from capturing simple patterns; Latte adds latent tokens to relax these correlations.
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SynGR: Unleashing the Potential of Cross-Modal Synergy for Generative Recommendation
SynGR is a synergistic generative recommendation method that masks dominant-modal tokens and uses contrastive learning to capture cross-modal synergies, outperforming prior methods on Amazon Arts, Games, and Instruments.