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Semantic Retrieval for Product Search in E-Commerce

arXiv 2606.01504 ↗First posted 31 May 2026cs.IR cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 19 of its 26 references that are in the map. First posted on arXiv 31 May 2026.

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19 of 26 references.

References · 19

  1. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
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  10. 2022Promptagator: Few-shot Dense Retrieval From 8 Examplescited by 233
  11. 2024Generative Representational Instruction Tuningcited by 227
  12. 2016Unbiased Learning-to-Rank with Biased Feedbackcited by 155
7 more
  1. 2020Embedding-based Retrieval in Facebook Searchcited by 155
  2. 2022Shopping Queries Dataset: A Large-Scale ESCI Benchmark for Improving Product Searchcited by 58
  3. 2019Semantic Product Searchcited by 51
  4. 2017Learning to Attend, Copy, and Generate for Session-Based Query Suggestioncited by 22
  5. 2021Learning a Product Relevance Model from Click-Through Data in E-Commercecited by 22
  6. 2024MOBIUS: Towards the Next Generation of Query-Ad Matching in Baidu's Sponsored Searchcited by 3
  7. 2021From Semantic Retrieval to Pairwise Ranking: Applying Deep Learning in E-commerce Searchcited by 2

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  1. 2025Automated Query-Product Relevance Labeling using Large Language Models for E-commerce Search
  2. 2026ZooWork-ShopRanker: An Open, Preference-Aligned E-Commerce Reranker
  3. 2026Synthetic Data Powers Product Retrieval for Long-tail Knowledge-Intensive Queries in E-commerce Search
  4. 2026Retrieve, Match, Escalate: Accurate and Scalable Product Linking with VLM-Distilled Cross-Encoders and Agentic VLMs
  5. 2026A Chain-of-Thought Approach to Semantic Query Categorization in e-Commerce Taxonomies
  6. 2026Efficient Generative Retrieval for E-commerce Search with Semantic Cluster IDs and Expert-Guided RL
  7. 2026VARG: Value-Aware and Ranking-Aligned Generative Retrieval for Dynamic E-commerce Search ↗
  8. 2025LREF: A Novel LLM-based Relevance Framework for E-commerce