# A Generative Re-ranking Model for List-level Multi-objective Optimization at Taobao

arXiv: https://arxiv.org/abs/2505.07197 · HTML: https://mlsanity.com/paper/2505.07197/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 May 2025.

## Built on by (most cited first, 6 of 6)

- [Synthetic Prefixes to Mitigate Bias in Real-Time Neural Query Autocomplete](https://mlsanity.com/paper/2510.01574/) (2025)
- [Scaling Search Relevance: Augmenting App Store Ranking with LLM-Generated Judgments](https://mlsanity.com/paper/2602.23234/) (2026)
- [FlashEvaluator: Expanding Search Space with Parallel Sequence-Level Evaluation](https://mlsanity.com/paper/2603.02565/) (2026)
- [Dual-Rerank: Fusing Causality and Utility for Industrial Generative Reranking](https://mlsanity.com/paper/2604.07420/) (2026)
- [Stochastic Primal-Dual Decoding for Multiobjective Generative Recommender Systems](https://mlsanity.com/paper/2607.19357/) (2026)
- [UniPolicy: Unified Objective-Specific Policies for Generative Search Advertising](https://mlsanity.com/paper/2609.20630/) (2026)

## Abstract (arXiv)

E-commerce recommendation systems aim to generate ordered lists of items for customers, optimizing multiple business objectives, such as clicks, conversions and Gross Merchandise Volume (GMV). Traditional multi-objective optimization methods like formulas or Learning-to-rank (LTR) models take effect at item-level, neglecting dynamic user intent and contextual item interactions. List-level multi-objective optimization in the re-ranking stage can overcome this limitation, but most current re-ranking models focus more on accuracy improvement with context. In addition, re-ranking is faced with the challenges of time complexity and diversity. In light of this, we propose a novel end-to-end generative re-ranking model named Sequential Ordered Regression Transformer-Generator (SORT-Gen) for the less-studied list-level multi-objective optimization problem. Specifically, SORT-Gen is divided into two parts: 1)Sequential Ordered Regression Transformer innovatively uses Transformer and ordered regression to accurately estimate multi-objective values for variable-length sub-lists. 2)Mask-Driven Fast Generation Algorithm combines multi-objective candidate queues, efficient item selection and diversity mechanism into model inference, providing a fast online list generation method. Comprehensive online experiments demonstrate that SORT-Gen brings +4.13% CLCK and +8.10% GMV for Baiyibutie, a notable Mini-app of Taobao. Currently, SORT-Gen has been successfully deployed in multiple scenarios of Taobao App, serving for a vast number of users.