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 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...
| Authors | Nikhil Kothari, Saksham Samdani, Ritam Mallick et al. |
|---|---|
| Primary category | cs.IR |
| Published | 31 May 2026 |
| Last updated at source | 31 May 2026 |
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