Reasoning over Semantic IDs Enhances Generative Recommendation
Recent advances in generative recommendation have leveraged pretrained LLMs by formulating sequential recommendation as autoregressive generation over a unified token space comprising language tokens and itemic identifiers, where each item is represented by a compact sequence of discrete tokens, namely Semantic IDs (SIDs). This SID-based formulation enables efficient decoding over large-scale item corpora and provides a natural interface for LLM-based recommenders to leverage rich world knowledge. Meanwhile, breakthroughs in LLM reasoning motivate reasoning-enhanced recommendation, yet effective reasoning over SIDs remains underexplored and challenging. Itemic tokens are not natively mean...
| Authors | Yingzhi He, Yan Sun, Junfei Tan et al. |
|---|---|
| Primary category | cs.IR |
| Published | 24 March 2026 |
| Last updated at source | 9 June 2026 |
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