Semantic Retrieval for Product Search in E-Commerce

• URL: http://arxiv.org/abs/2606.01504v1
• Date: Sun, 31 May 2026 23:59:32 GMT
• Title: Semantic Retrieval for Product Search in E-Commerce
• Abstract summary: We present a Siamese LLM dual-encoder trained through a two-stage pipeline.<n>A preference optimization objective extends Bradley-Terry to variable-sized graded relevance groups.<n>The resulting system accurately retrieves exact matches while correctly ordering substitutes and complementary products.
• Score: 2.101206887508497
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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