In modern multi-stage recommendation systems, reranking plays a critical role by modeling contextual information. Due to inherent challenges such as the combinatorial space complexity, an increasing number of methods adopt the generative paradigm: the generator produces the optimal list during inference, while an evaluator guides the generator's optimization during the training phase. However, these methods still face two problems. Firstly, these generators fail to produce optimal generation results due to the lack of both local and global perspectives, regardless of whether the generation strategy is autoregressive or non-autoregressive. Secondly, the goal inconsistency problem between the generator and the evaluator during training complicates the guidance signal and leading to suboptimal performance. To address these issues, we propose the \textbf{N}ext-\textbf{S}cale \textbf{G}eneration \textbf{R}eranking (NSGR), a tree-based generative framework. Specifically, we introduce a next-scale generator (NSG) that progressively expands a recommendation list from user interests in a coarse-to-fine manner, balancing global and local perspectives. Furthermore, we design a multi-scale neighbor loss, which leverages a tree-based multi-scale evaluator (MSE) to provide scale-specific guidance to the NSG at each scale. Extensive experiments on public and industrial datasets validate the effectiveness of NSGR. And NSGR has been successfully deployed on the Meituan food delivery platform.

翻译：在现代多阶段推荐系统中，重排通过建模上下文信息发挥着关键作用。由于组合空间复杂度等固有挑战，越来越多的方法采用生成式范式：生成器在推理阶段生成最优列表，而评估器在训练阶段引导生成器的优化。然而，这些方法仍面临两个问题。首先，无论生成策略是自回归还是非自回归，这些生成器因缺乏局部和全局视角而无法产生最优生成结果。其次，训练过程中生成器与评估器之间的目标不一致问题导致引导信号复杂化，进而造成次优性能。为解决这些问题，我们提出**下一尺度生成重排**（NSGR）——一种基于树结构的生成式框架。具体而言，我们引入下一尺度生成器（NSG），该生成器以从粗到细的方式根据用户兴趣逐步扩展推荐列表，从而平衡全局与局部视角。此外，我们设计多尺度邻居损失，利用基于树结构的多尺度评估器（MSE）在每个尺度上为NSG提供尺度特定的引导。在公开数据集与工业数据集上的大量实验验证了NSGR的有效性。目前NSGR已在美团外卖平台成功部署。