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LLM dual-encoder improves e-commerce product search accuracy

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new semantic retrieval system for e-commerce product search, designed to handle imprecise user queries and large product catalogs. The system utilizes a Siamese LLM dual-encoder trained in a two-stage process, beginning with contrastive learning and progressing to a preference optimization objective called Relative Odds Alignment for Retrieval (ROAR). This approach aims to accurately retrieve exact matches while effectively ranking substitute and complementary products, with demonstrated success through large-scale A/B testing. AI

IMPACT Enhances product search capabilities by improving relevance and ranking for e-commerce platforms.

RANK_REASON The cluster contains an academic paper detailing a new method for semantic retrieval in e-commerce.

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Nikhil Kothari, Saksham Samdani, Ritam Mallick, Praveen Gupta, Ankit Vijay, Surender Kumar ·

    Semantic Retrieval for Product Search in E-Commerce

    arXiv:2606.01504v1 Announce Type: cross Abstract: Semantic retrieval in e-commerce must handle short, noisy, and colloquial queries over large product catalogs with fine-grained attribute distinctions. We present a Siamese LLM dual-encoder trained through a two-stage pipeline: co…

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Surender Kumar ·

    Semantic Retrieval for Product Search in E-Commerce

    Semantic retrieval in e-commerce must handle short, noisy, and colloquial queries over large product catalogs with fine-grained attribute distinctions. We present a Siamese LLM dual-encoder trained through a two-stage pipeline: contrastive learning with a false-negative margin ma…

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