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

• URL: http://arxiv.org/abs/2505.07197v1
• Date: Mon, 12 May 2025 03:01:14 GMT
• Title: A Generative Re-ranking Model for List-level Multi-objective Optimization at Taobao
• Authors: Yue Meng, Cheng Guo, Yi Cao, Tong Liu, Bo Zheng,
• Abstract summary: 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.<n>SORT-Gen has been successfully deployed in multiple scenarios of Taobao App, serving for a vast number of users.
• Score: 20.11584617315975
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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