Alibaba finds generative recommendation still has hard limits on cold items
_reachsumit · x · 2026-07-24
Alibaba studies why generative recommendation still struggles with cold items
Alibaba explores Semantic-ID generation for generative recommendation under an absolute-time protocol.
- The paper shows that cold-item reachability is bounded by the learned token paths.
- In other words, even when generation is used for recommendation, the model may still have structural limits in surfacing new or rarely seen items.
- The post links to the paper for the full method and analysis.
More from Research
- PNAS paper shows a tiny billiard-ball system is a universal computer — undecidability lives in two dimensions — eigensteve · 2026-09-11
- New paper: Absolute pose estimation from affine cues and gravity direction — ducha_aiki · 2026-09-11
- LoMa Paper Ships REALLY HardPairs Dataset, Accepted at ECCV 2026 — ducha_aiki · 2026-09-11
- Johns Hopkins Launches Full-Stack Hands-on Robot Learning Class with SO-101 Arm Kits — _krishna_murthy · 2026-09-11
- SyncWorld: In-Context Robot World Model Simulates Unseen Views and Embodiments Zero-Shot — ChongZzZhang · 2026-09-11
- A 3D Pose Dataset for Dogs Released — ducha_aiki · 2026-09-11