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

• URL: http://arxiv.org/abs/2609.04961v1
• Date: Fri, 04 Sep 2026 10:09:04 GMT
• Title: SAM-D2Q: Aligning Multimodal Doc2Query with Search Demand and Conversion for E-commerce
• Abstract summary: E-commerce search often suffers from vocabulary mismatch between user queries and merchant-authored product titles.<n>Traditional methods are text-only and not optimized for e-commerce business objectives.<n>We propose E-commerce Search Multimodal Doc2Query (SAM-D2Q), a business-aligned multimodal document expansion framework.
• Score: 11.766307104320726
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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%.

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