标题： 布局即模型：生成推荐中动作项耦合的研究 显示英文标题

标题： The Layout Is the Model: On Action-Item Coupling in Generative Recommendation

摘要： 生成推荐（GR）模型将用户的历史交互视为一个序列，以自回归方式预测。 当同时建模物品和动作（例如，观看时间、购买、评论）时，布局——即物品/动作标记的顺序和可见性——关键决定了模型可以使用哪些信息以及如何泛化。 我们基于基本原理对GR中的标记布局进行了统一研究：（P1）在输入/输出空间中最大化物品/动作信号，（P2）保留“给定物品的动作”的条件关系，（P3）无信息泄露。 虽然交错布局（其中物品和动作占据单独的标记）自然满足这些原则，但它也会增加序列长度，导致更大的训练/推理成本。 在非交错方面，我们设计了一种新颖且有效的方法，滞后动作条件（LAC），表面上看起来奇怪，但与设计原则一致，能够实现强大的准确性。 在公开数据集和大规模生产日志上的全面实验评估了不同的布局选项，并实证验证了设计原则。 我们提出的非交错方法LAC，在显著更低的FLOPs下实现了竞争力或更优的性能。 我们的发现为构建准确且高效的GR系统提供了可操作的指导。 显示英文摘要

摘要： Generative Recommendation (GR) models treat a user's interaction history as a sequence to be autoregressively predicted. When both items and actions (e.g., watch time, purchase, comment) are modeled, the layout-the ordering and visibility of item/action tokens-critically determines what information the model can use and how it generalizes. We present a unified study of token layouts for GR grounded in first principles: (P1) maximize item/action signal in both input/output space, (P2) preserve the conditioning relationship "action given item" and (P3) no information leakage. While interleaved layout (where item and action occupy separate tokens) naturally satisfies these principles, it also bloats sequence length with larger training/inference cost. On the non-interleaved front, we design a novel and effective approach, Lagged Action Conditioning (LAC), which appears strange on the surface but aligns well with the design principles to yield strong accuracy. Comprehensive experiments on public datasets and large-scale production logs evaluate different layout options and empirically verifies the design principles. Our proposed non-interleaved method, LAC, achieves competitive or superior quality at substantially lower FLOPs than interleaving. Our findings offer actionable guidance for assembling GR systems that are both accurate and efficient.