SAM-D2Q: Aligning Multimodal Doc2Query with Search Demand and Conversion for E-commerce

2026-09-04

AI Summary

针对电商搜索中用户查询与商品标题间的词汇不匹配问题，提出SAM-D2Q框架，通过多模态细调、数据增强及强化学习优化生成伪查询，提升检索性能和商业指标。

Abstract

E-commerce search often suffers from vocabulary mismatch between user queries and merchant-authored product titles, since short titles cannot fully cover diverse user expressions or visual product attributes. Although Doc2Query alleviates this issue by generating pseudo-queries for document expansion, traditional methods are text-only and not optimized for e-commerce business objectives. As a result, they may produce semantically plausible but commercially ineffective expansions and miss key attributes present in product images. To this end, we propose E-commerce Search-Aligned Multimodal Doc2Query (SAM-D2Q), a business-aligned multimodal document expansion framework for e-commerce search under Boolean retrieval constraints. SAM-D2Q consists of three stages: (1) task-adapted multimodal supervised fine-tuning to enhance vision-language understanding of product titles, images, and user queries; (2) multimodal data augmentation to improve perception of key visual attributes and expansion coverage; and (3) reinforcement-learning-based preference alignment toward search business objectives, encouraging the model to generate pseudo-queries that better match user intent and commercial value. Offline experiments show that SAM-D2Q substantially improves retrieval performance over traditional Doc2Query methods. Deployed in the AliExpress production search system, SAM-D2Q improves online business metrics, increasing GMV by +3.38% and Pay Count by +2.27%.

Problem

• vocabulary mismatch
• user queries
• product titles
• visual attributes
• e-commerce search

Innovation

• Multimodal Doc2Query
• E-commerce Search
• Reinforcement Learning
• Business Objectives