Generative Recommendation has revolutionized recommender systems by reformulating retrieval as a sequence generation task over discrete item identifiers. Despite the progress, existing approaches typically rely on static, decoupled tokenization that ignores collaborative signals. While recent methods attempt to integrate collaborative signals into item identifiers either during index construction or through end-to-end modeling, they encounter significant challenges in real-world production environments. Specifically, the volatility of collaborative signals leads to unstable tokenization, and current end-to-end strategies often devolve into suboptimal two-stage training rather than achieving true co-evolution. To bridge this gap, we propose PIT, a dynamic Personalized Item Tokenizer framework for end-to-end generative recommendation, which employs a co-generative architecture that harmonizes collaborative patterns through collaborative signal alignment and synchronizes item tokenizer with generative recommender via a co-evolution learning. This enables the dynamic, joint, end-to-end evolution of both index construction and recommendation. Furthermore, a one-to-many beam index ensures scalability and robustness, facilitating seamless integration into large-scale industrial deployments. Extensive experiments on real-world datasets demonstrate that PIT consistently outperforms competitive baselines. In a large-scale deployment at Kuaishou, an online A/B test yielded a substantial 0.402% uplift in App Stay Time, validating the framework's effectiveness in dynamic industrial environments.

翻译：生成式推荐通过将检索任务重新定义为基于离散物品标识符的序列生成任务，彻底革新了推荐系统。尽管取得了进展，现有方法通常依赖于静态、解耦的分词策略，忽略了协同信号。虽然近期方法尝试在索引构建阶段或通过端到端建模将协同信号融入物品标识符，但它们在现实生产环境中面临显著挑战。具体而言，协同信号的波动性导致分词结果不稳定，而当前的端到端策略往往退化为次优的两阶段训练，未能实现真正的协同演化。为弥补这一差距，我们提出了PIT——一种面向端到端生成式推荐的动态个性化物品分词器框架。该框架采用协同生成架构，通过协同信号对齐协调协同模式，并借助协同演化学习将物品分词器与生成式推荐器同步优化。这使得索引构建与推荐过程能够实现动态、联合、端到端的协同演化。此外，一对多波束索引确保了系统的可扩展性与鲁棒性，便于无缝集成至大规模工业部署。在真实数据集上的大量实验表明，PIT始终优于现有竞争基线。在快手平台的大规模部署中，在线A/B测试实现了0.402%的App停留时长显著提升，验证了该框架在动态工业环境中的有效性。