EPIC: Explicit Posterior Item Conditioning for Semantic ID Diffusion Recommendation
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 an...
| Authors | Tuan-Binh Tran, Thanh Tam Nguyen, Quoc Viet Hung Nguyen et al. |
|---|---|
| Primary category | cs.IR |
| Published | 3 September 2026 |
| Last updated at source | 3 September 2026 |
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