MuSID-KD paper distills semantic-ID generative recommenders by ranking impact
_reachsumit · x · 2026-10-28
arXiv paper MuSID-KD improves knowledge distillation for semantic-ID (SID) generative recommendation. It identifies two flaws in conventional objectives: item-level alignment fails to preserve teacher rankings among candidate groups sharing SID prefixes, and uniform alignment across SID positions ignores differing ranking impact. The method combines HCDA (hierarchical candidate distribution alignment across SID levels) and RIGD (allocating supervision by measured reduction in pairwise ranking surrogate loss). Experiments across real-world datasets and three lightweight student architectures show better accuracy at lower inference cost.
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