HASTE: Hierarchical and Attribute-aware Supervision for Generative Recommendation
Generative recommendation models have shown promising results by representing items as semantic IDs and generating these IDs autoregressively. We introduce an approach showing that explicitly incorporating hierarchical and attribute-level supervision into generative recommendation can simultaneously improve training efficiency and long-tail recommendation performance. Existing approaches typically optimize semantic ID prediction with a standard causal cross-entropy objective, which does not explicitly exploit the hierarchical structure of semantic IDs and can require substantial training to learn effective item representations. Moreover, data-sparse tail items often suffer from poor repre...
| Authors | Tripti Shukla, Shwetha Somasundaram, Akshat Gupta et al. |
|---|---|
| Primary category | cs.IR |
| Published | 12 October 2026 |
| Last updated at source | 12 October 2026 |
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