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New framework enhances e-commerce semantic search relevance

By PulseAugur Editorial · [1 sources] ·

A new research paper introduces "Mine and Refine," a two-stage contrastive training framework designed to improve semantic search retrieval in e-commerce. This method addresses challenges such as noisy engagement signals, difficulties in mining hard negative samples, and unstable similarity score separation across different relevance levels. The framework utilizes a lightweight LLM for scalable labeling and employs label-aware supervised contrastive learning and a multi-level extension of circle loss. When deployed in production e-commerce search, the approach demonstrated significant improvements in user engagement, gross order value, and overall relevance metrics. AI

IMPACT This framework could significantly improve the effectiveness and user engagement of e-commerce search systems by refining retrieval accuracy.

RANK_REASON The cluster contains a research paper detailing a new methodology for improving semantic search retrieval. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework enhances e-commerce semantic search relevance

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The cluster contains a research paper detailing a new methodology for improving semantic search retrieval. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Jiaqi Xi, Raghav Saboo, Luming Chen, Johny Rufus, Aditya Dodda, Ved Sampath, Kenny Chi, Elyse Winer, Akshad Viswanathan, Martin Wang, Sudeep Das ·

    Mine and Refine: Optimizing Graded Relevance in E-commerce Semantic Search Retrieval

    arXiv:2602.17654v2 Announce Type: replace-cross Abstract: Embedding-based retrieval (EBR) for large-scale e-commerce search faces three intertwined challenges: graded (non-binary) relevance where engagement signals are noisy and intent-varying while business relevance guidelines …

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