Learning Better Reasoning for Generative Recommendation with Semantic IDs
Generative recommendation reformulates item retrieval as sequence generation, allowing a unified model to directly generate the next item from a user's interaction history. Semantic IDs further make this paradigm effective and scalable by representing each item as discrete codes, enabling knowledge sharing among semantically related items. Recent studies introduce explicit reasoning before Semantic-ID generation, helping models summarize user interests and infer possible preference transitions. However, reasoning is not inherently beneficial: Inaccurate or uninformative reasoning may mislead subsequent item generation and ultimately degrade recommendation performance. This raises a centra...
| Authors | Mengdan Zhu, Yufan Zhao, Sophie Di et al. |
|---|---|
| Primary category | cs.IR |
| Published | 24 September 2026 |
| Last updated at source | 24 September 2026 |
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