Generative recommendation (GR) has recently emerged as a promising paradigm for industrial recommendations. GR leverages Semantic IDs (SIDs) to reduce the encoding-decoding space and employs the Next Token Prediction (NTP) framework to explore scaling laws. However, existing GR methods suffer from two critical issues: (1) a \textbf{seesaw phenomenon} in multi-business scenarios arises due to NTP's inability to capture complex cross-business behavioral patterns; and (2) a unified SID space causes \textbf{representation confusion} by failing to distinguish distinct semantic information across businesses. To address these issues, we propose Multi-Business Generative Recommendation (MBGR), the first GR framework tailored for multi-business scenarios. Our framework comprises three key components. First, we design a Business-aware semantic ID (BID) module that preserves semantic integrity via domain-aware tokenization. Then, we introduce a Multi-Business Prediction (MBP) structure to provide business-specific prediction capabilities. Furthermore, we develop a Label Dynamic Routing (LDR) module that transforms sparse multi-business labels into dense labels to further enhance the multi-business generation capability. Extensive offline and online experiments on Meituan's food delivery platform validate MBGR's effectiveness, and we have successfully deployed it in production.

翻译：生成式推荐（GR）近期已成为工业推荐中一种具有前景的范式。GR通过采用语义ID（SID）压缩编码-解码空间，并利用下一词元预测（NTP）框架探索缩放定律。然而，现有GR方法存在两个关键问题：（1）在多业务场景下，由于NTP无法捕捉复杂的跨业务行为模式，导致出现**跷跷板现象**；（2）统一的SID空间因未能区分不同业务间差异化的语义信息，引发**表示混淆**。为此，我们提出面向多业务场景的生成式推荐方法MBGR——首个专为多业务场景设计的GR框架。该框架包含三个核心组件：首先设计业务感知语义ID（BID）模块，通过领域感知词元化保证语义完整性；其次引入多业务预测（MBP）结构，提供业务专属的预测能力；最后开发标签动态路由（LDR）模块，将稀疏的多业务标签转换为稠密标签，进一步强化多业务生成能力。在美团外卖平台上的大规模离线和在线实验验证了MBGR的有效性，目前该方法已成功部署至生产环境。