GOD: Enhancing Generalization via Deep Grafting for Sequential Recommendation
Sequential recommenders often struggle with sparse and noisy histories, limiting generalization to unseen interactions. Knowledge distillation mitigates this by transferring dense supervision from a teacher to a student. However, most distillation methods run teacher and student independently, then match student outputs or representations to the teacher. Such supervision entangles student-component effects, blurring whether weak generalization stems from unreliable embeddings, overfitted encoding, or co-adaptation to sparse histories. In this paper, we propose Graft-Oriented Distillation (GOD), a component-level distillation framework for improved generalization through grafting. Grafting...
| Authors | WooJoo Kim, JunYoung Kim, JaeHyung Lim et al. |
|---|---|
| Primary category | cs.IR |
| Published | 17 August 2026 |
| Last updated at source | 25 August 2026 |
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