Semantic IDs serve as a key component in generative recommendation systems. They not only incorporate open-world knowledge from large language models (LLMs) but also compress the semantic space to reduce generation difficulty. However, existing methods suffer from two major limitations: (1) the lack of contextual awareness in generation tasks leads to a gap between the Semantic ID codebook space and the generation space, resulting in suboptimal recommendations; and (2) suboptimal quantization methods exacerbate semantic loss in LLMs. To address these issues, we propose Dual-Flow Orthogonal Semantic IDs (DOS) method. Specifically, DOS employs a user-item dual flow-framework that leverages collaborative signals to align the Semantic ID codebook space with the generation space. Furthermore, we introduce an orthogonal residual quantization scheme that rotates the semantic space to an appropriate orientation, thereby maximizing semantic preservation. Extensive offline experiments and online A/B testing demonstrate the effectiveness of DOS. The proposed method has been successfully deployed in Meituan's mobile application, serving hundreds of millions of users.

翻译：语义ID是生成式推荐系统的核心组件。它不仅融合了来自大语言模型（LLM）的开放世界知识，还能压缩语义空间以降低生成难度。然而，现有方法存在两大局限：（1）生成任务中缺乏上下文感知，导致语义ID码本空间与生成空间之间存在鸿沟，从而产生次优推荐；（2）次优的量化方法加剧了LLM中的语义损失。为解决这些问题，我们提出了双流正交语义ID（DOS）方法。具体而言，DOS采用用户-物品双流框架，利用协同信号将语义ID码本空间与生成空间对齐。此外，我们引入了一种正交残差量化方案，将语义空间旋转至合适方向，从而最大化语义保留。大量的离线实验和在线A/B测试验证了DOS的有效性。该方法已成功部署于美团移动应用程序，为数亿用户提供服务。