Next-Scale Generative Reranking: A Tree-based Generative Rerank Method at Meituan

TL;DR

The paper introduces NSGR, a tree-based generative reranking framework that improves multi-stage recommendation systems by addressing generator-evaluator inconsistencies and incorporating multi-scale guidance.

Contribution

It proposes a novel next-scale generator and a multi-scale evaluator to enhance recommendation list generation with global and local perspectives.

Findings

NSGR outperforms existing reranking methods on public datasets.

NSGR achieves significant improvements in recommendation quality.

Successful deployment of NSGR on Meituan's food delivery platform.

Abstract

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.