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A large-scale reproducibility study to systematically investigate the impact of semantic ID design on generative recommendation under a unified experimental framework provides a controlled and reproducible understanding of semantic ID design and offers practical insights for future generative recommender systems.
Generative recommendation's cold-start gains are often illusory, inflated by inconsistent evaluation and confounding design choices like model scale and identifier design.
