发表机构
University of Illinois Chicago; Amazon（伊利诺伊大学芝加哥分校; 亚马逊）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究挑战了简单哈希方法在生成式推荐中性能低下的共识，提出FLASH两阶段框架，通过并行解码和显式语义对齐重振SimHash标记化，无需训练即可在多个数据集上实现最先进性能，并在冷启动场景中展现更强泛化能力。
AI 中文摘要
基于语义ID的生成式推荐将每个项目表示为离散标记的序列，从而实现对项目语义的结构化建模。一个关键挑战是构建既具有语义表达力又具有计算效率的语义ID。尽管近期方法倾向于复杂的量化学习，但诸如SimHash之类的简单哈希方法被广泛认为本质上较为低劣。在本工作中，我们挑战这一共识，表明明显的性能差距并非源于哈希的固有限制，而是源于与自回归解码的结构性不匹配，以及刚性离散化过程中不可避免的信息损失。基于这一见解，我们提出了FLASH，一个两阶段框架，通过并行解码和显式语义对齐来重振无需训练的SimHash标记化。尽管其简单性，FLASH在多个数据集上实现了最先进的性能，且无需任何标记器训练，同时在冷启动场景中展现出更强的泛化能力。值得注意的是，我们证明语义对齐在不同范式中是一种普遍有效的机制。我们的发现表明，通过兼容的解码和语义基础，简单高效的标记器在生成式推荐中可以达到与复杂学习对应物相当的性能。我们的代码可在以下网址获取：https://this URL。
英文摘要
Semantic ID-based generative recommendation represents each item as a sequence of discrete tokens, enabling structured modeling of item semantics. A critical challenge is constructing semantic IDs that are both semantically expressive and computationally efficient. While recent approaches favor complex learned quantization, simple hashing-based methods such as SimHash are widely regarded as fundamentally inferior. In this work, we challenge this consensus by showing that the apparent performance gap does not stem from inherent limitations of hashing, but rather from a structural mismatch with autoregressive decoding, coupled with the inevitable information loss during rigid discretization. Based on this insight, we propose FLASH, a two-stage framework that revitalizes training-free SimHash tokenization through parallel decoding and explicit semantic alignment. Despite its simplicity, FLASH achieves state-of-the-art performance across multiple datasets without requiring any tokenizer training, while exhibiting stronger generalization in cold-start scenarios. Notably, we demonstrate that semantic alignment acts as a universally effective mechanism across diverse paradigms. Our findings suggest that, with compatible decoding and semantic grounding, simple and efficient tokenizers can achieve performance comparable to complex learned counterparts in generative recommendation. Our code is available at https://github.com/KevinC2015/Flash.
CommentsAccepted at NeurIPS 2026. Code: https://github.com/KevinC2015/Flash
