DOS: 基于双流正交语义ID的美团推荐系统
DOS: Dual-Flow Orthogonal Semantic IDs for Recommendation in Meituan
AI总结：
DOS通过双流正交语义ID方法提升推荐系统效果，有效解决语义空间对齐与量化损失问题。
摘要：
语义ID是生成推荐系统中的关键组件。它们不仅整合了大型语言模型（LLMs）的开放世界知识，还压缩了语义空间以降低生成难度。然而，现有方法存在两个主要局限：（1）生成任务中缺乏上下文意识导致语义ID代码本空间与生成空间之间存在差距，从而导致推荐效果不佳；（2）次优的量化方法加剧了LLMs中的语义损失。为了解决这些问题，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代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，将语义空间旋转到合适的方向，从而最大化语义保留。广泛的离线实验和在线A/B测试证明了DOS的有效性。所提出的方法已在美团移动应用中成功部署，服务数亿用户。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
