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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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 new framework for generative recommendation that constrains overreliance on single modalities to exploit synergistic cross-modal information for better item semantics and user preference modeling.
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 new framework for generative recommendation that constrains overreliance on single modalities to exploit synergistic cross-modal information for better item semantics and user preference modeling.