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New SAM-D2Q framework boosts e-commerce search with multimodal Doc2Query

By PulseAugur Editorial · [1 sources] ·

A new framework called SAM-D2Q has been developed to improve e-commerce search by generating more effective pseudo-queries for product listings. This multimodal approach incorporates product images and user search data, going beyond traditional text-only methods. When implemented in AliExpress's search system, SAM-D2Q led to a 3.38% increase in Gross Merchandise Volume (GMV) and a 2.27% rise in Pay Count. AI

IMPACT Enhances e-commerce search effectiveness by better matching product listings to user queries and commercial value.

RANK_REASON The item describes a new research paper detailing a novel framework for improving e-commerce search. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New SAM-D2Q framework boosts e-commerce search with multimodal Doc2Query

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The item describes a new research paper detailing a novel framework for improving e-commerce search. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Xiaoyi Zeng ·

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

    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 f…

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