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arXiv AI By Song-Li Wu, Weinan Gan, Zhaocheng Du, Xianquan Wang, Jingyi Wang

FineSID: Scalable and Efficient Semantic Identifier Learning for Generative Recommendation

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FineSID introduces a new quantization framework for semantic identifier learning in generative recommendation systems. By replacing the traditional Top‑1 hard assignment with a soft, differentiable approach, it distributes gradient updates across all codewords, leading to balanced codebook optimization and reduced identifier collisions. Experiments on public benchmarks show that FineSID improves codebook utilization and recommendation accuracy without relying on complex initialization strategies.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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