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Semantic Retrieval for Product Search in E-Commerce

arXiv CS Tuesday 02 June 2026, 04:00 UTC By Nikhil Kothari, Saksham Samdani, Ritam Mallick, Praveen Gupta, Ankit Vijay, Surender Kumar 1 min read
Key Points

arXiv:2606.01504v1 Announce Type: new 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: contrastive learning with a false-negative margin mask to prevent penalization of near-duplicate products, followed by Relative Odds Alignment for Retrieval (ROAR), a preference optimization objective that extends...

arXiv:2606.01504v1 Announce Type: new 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: contrastive learning with a false-negative margin mask to prevent penalization of near-duplicate products, followed by Relative Odds Alignment for Retrieval (ROAR), a preference optimization objective that extends Bradley-Terry to variable-sized graded relevance groups via consecutive odds-ratio margins. The training corpus mirrors this progression - substitute query-product pairs provide coarse semantic supervision in Stage 1 and graded relevance annotations drive fine-grained ranking in Stage 2. The resulting system accurately retrieves exact matches while correctly ordering substitutes and complementary products, with gains confirmed across query-frequency strata and business verticals, and statistical significance validated through live A/B deployment at scale.
Semantic Retrieval for Product Search (ORG) E-Commerce (ORG) Siamese (ORG) Bradley-Terry (PERSON) Stage 1 (EVENT) Stage 2 (EVENT)
Originally published by arXiv CS Read original →

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