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Life sciences · Preprint
arXiv · September 3, 2026
Posted before peer review. The findings may change or fail to hold.
EPIC is a novel method for semantic ID diffusion-based recommendation that integrates explicit item-level competition into token denoising using personalized posterior distributions. The preprint reports consistent improvements over baselines on four Amazon benchmarks, but lacks peer review, detailed ablations, and quantified effect sizes necessary to assess practical impact.
Preprint. Amazon e-commerce recommendation datasets. Intervention: Explicit Posterior Item Conditioning (EPIC): introduction of explicit item-level competition into SID denoising using personalized posterior distributions over feasible candidate items projected to unresolved SID positions. Compared with: Strong baselines in semantic ID generative recommendation.
EPIC shows consistent improvements over strong baselines on four Amazon benchmarks Gains primarily arise from personalized transition evidence that preserves promising item hypotheses during denoising
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This is an unrefereed arXiv preprint describing a novel machine-learning method for recommendation systems; it reports empirical results on benchmark datasets but has not undergone peer review.
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Semantic ID (SID) generative recommendation predicts the next item by generating a short tuple of discrete tokens. Recent masked-diffusion methods improve this process through bidirectional context and flexible decoding, yet recommendation ultimately requires selecting among complete catalog items. At each denoising step, a partial SID can correspond to multiple feasible items, while existing methods primarily reason through position-wise token predictions. We propose Explicit Posterior Item Conditioning (EPIC), which introduces explicit item-level competition into SID denoising. EPIC constructs a personalized posterior over feasible candidate items using the current generation context and the user's recent interactions, then projects this distribution back to unresolved SID positions to guide subsequent token decisions. The pretrained backbone remains frozen and requires no additional decoder forward pass. Experiments on four Amazon benchmarks show consistent improvements over strong baselines, while diagnostic analyses indicate that the gains primarily arise from personalized transition evidence that preserves promising item hypotheses during denoising.
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