Alibaba's Privileged Self-Distillation: Leveraging Future Interactions for Sequential Recommendation
_reachsumit · x · 2026-07-30
Alibaba proposes the Privileged Self-Distillation (PSD) framework, which enhances sequential recommenders by incorporating "future interaction" information during the training phase.
- Core Insight: Interactions following a target item often reveal how user intent evolves, making the target easier to interpret. PSD treats these future interactions as training-only privileged information, unavailable at inference, to maintain alignment between training and prediction.
- Mechanism: Applies two attention masks to the same backbone. A future-aware view yields a privileged teacher distribution, while a prefix-only view yields the student distribution used for deployment. Distillation converts future information into effective training supervision.
- Architectural Advantage: Both views share the same backbone, meaning the teacher's advantage is purely informational rather than architectural, removing the need for a separately pretrained teacher model.
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