提升美团推荐系统语义编码，让生成更精准。
DOS: Dual-Flow Orthogonal Semantic IDs for Recommendation in Meituan
- 双流框架融合用户与物品信号，对齐语义空间。
- 正交残差量化减少大模型语义损失，提升保留率。
- 已在美团数亿用户中落地，线上效果显著。
语义ID是生成式推荐系统的核心组件，既能融入大语言模型的开放世界知识，又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) 生成任务缺乏上下文感知，导致语义ID词典空间与生成空间不一致，影响推荐效果；(2) 量化方法不佳加剧了大模型的语义损失。为此，我们提出双流正交语义ID（DOS）方法。DOS采用用户-物品双流框架，利用协同信号对齐语义ID词典空间与生成空间。同时引入正交残差量化方案，将语义空间旋转至最优方向，以最大化语义保留。大量离线实验与线上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，服务数亿用户。
原文摘要 · Abstract (English)
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.
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