VaLiDRec: Variable-Length LLM-Aligned Semantic IDs for Generative Recommendation
Generative recommendation commonly represents items using fixed-length semantic identifiers (SIDs) constructed through clustering and quantization. However, these artificial codes may overcompress item semantics, remain misaligned with pretrained LLM vocabularies, and require costly autoregressive decoding. In light of this, we propose VaLiDRec, a generative recommendation framework based on variable-length, LLM-aligned semantic identifiers. VaLiDRec constructs SIDs directly from informative native LLM vocabulary tokens via token importance estimation, semantic-quality-aware pruning, and collision-aware refinement, allowing identifier lengths to adapt to item semantic complexity. To model...
| Authors | Shutong Qiao, Wei Yuan, Tong Chen et al. |
|---|---|
| Primary category | cs.IR |
| Published | 28 July 2026 |
| Last updated at source | 28 July 2026 |
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