HASTE: Hierarchical and Attribute-aware Supervision for Generative Recommendation
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.
ProofPaper ↗
Key points
- Generative recommendation models have shown promising results by representing items as semantic IDs and generating these IDs autoregressively.
- Moreover, data-sparse tail items often suffer from poor representations due to limited supervision, resulting in weaker long-tail recommendation performance.
- HASTE's Hierarchical Loss explicitly exploits the multi-level structure of semantic IDs, providing supervision at each level of the item hierarchy and substantially accelerating training convergence.
- HASTE improves recommendation performance across datasets and item-frequency regimes.
Sources (1)
- [1]HASTE: Hierarchical and Attribute-aware Supervision for Generative RecommendationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 12, 08:25 PM
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.
Generative recommendation models have shown promising results by representing items as semantic IDs and generating these IDs autoregressively.
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