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E-commerce search boosted by new generative retrieval research · 4 sources tracked

By PulseAugur Editorial · [6 sources] ·

Four research papers published on arXiv propose novel methods for generative retrieval in e-commerce search. These approaches aim to improve product search accuracy and user engagement by jointly training embedding models and codebooks, incorporating product cluster information, and refining relevance metrics. The proposed frameworks address issues like error accumulation, noisy engagement signals, and the need for better query-intent consistency, with some demonstrating significant improvements in retrieval metrics and business outcomes in production environments. AI

IMPACT These research papers propose advancements in generative retrieval for e-commerce, potentially leading to more accurate product search and improved user engagement.

RANK_REASON The cluster consists of multiple academic papers published on arXiv detailing new research methodologies.

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 6 sources. How we write summaries →

E-commerce search boosted by new generative retrieval research · 4 sources tracked

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The cluster consists of multiple academic papers published on arXiv detailing new research methodologies.
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6 independent sources
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paper, product, infra
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55 days old
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Full methodology in our editorial standards.

COVERAGE [6]

  1. arXiv cs.AI TIER_1 English(EN) · Songtao Fang, Zihao Xu, Shaowei Wei, Jin Zhang, Zhuojun Wang ·

    Generative Retrieval for E-commerce: Jointly Learning Embedding and Codebook with Same Product Cluster

    arXiv:2608.30606v1 Announce Type: cross Abstract: With the development of large language models (LLMs), generative retrieval is becoming increasingly important in e-commerce scenarios. Current mainstream approaches typically use a two-stage training strategy: first train a produc…

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Zhuojun Wang ·

    Generative Retrieval for E-commerce: Jointly Learning Embedding and Codebook with Same Product Cluster

    With the development of large language models (LLMs), generative retrieval is becoming increasingly important in e-commerce scenarios. Current mainstream approaches typically use a two-stage training strategy: first train a product embedding model, and then learn a codebook that …

  3. Hugging Face Daily Papers TIER_1 English(EN) ·

    Generative Retrieval for E-commerce: Jointly Learning Embedding and Codebook with Same Product Cluster

    With the development of large language models (LLMs), generative retrieval is becoming increasingly important in e-commerce scenarios. Current mainstream approaches typically use a two-stage training strategy: first train a product embedding model, and then learn a codebook that …

  4. 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 …

  5. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Fuzhen Zhuang ·

    ICEGR: An Intent-Coherent End-to-End Generative Retrieval Framework for E-commerce Search

    Generative Retrieval (GR) is promising for e-commerce search, yet existing methods struggle to maintain query-intent consistency throughout the training pipeline. First, semantic ID (SID) construction based on static product information limits the ability of SIDs to encode produc…

  6. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Fuzhen Zhuang ·

    ICEGR: An Intent-Coherent End-to-End Generative Retrieval Framework for E-commerce Search

    Generative Retrieval (GR) is promising for e-commerce search, yet existing methods struggle to maintain query-intent consistency throughout the training pipeline. First, semantic ID (SID) construction based on static product information limits the ability of SIDs to encode produc…

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