English(EN) EAGER: Enrich-and-Align Generative Query Recommendation from Clicked Items in E-commerce Search

EAGER框架利用LLM增强电子商务查询推荐

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-07 07:44

研究人员开发了EAGER，一个新颖的两阶段框架，旨在为电子商务搜索生成更相关的查询建议。第一阶段，富化，使用监督微调和逐步增加信息丰富度和推理深度的课程，结合了合理性增强和多样性正则化。第二阶段，对齐，采用GRPO训练，并结合了业务信号和基于点击的偏好的混合奖励系统。EAGER在离线实验和在线A/B测试中均显示出显著的有效性，并已被一个主要的电子商务平台部署到生产环境中。 AI

影响该框架可以显著提高电子商务中搜索结果的相关性和个性化，从而改善用户体验和转化率。

排序理由这是一篇详细介绍查询推荐新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.IR (Information Retrieval) 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

Signal score: 0 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 这是一篇详细介绍查询推荐新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, product, infra; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: 2 days old; Coverage has settled into its steady-state source set.

完整方法见我们的编辑标准。

报道来源 [1]

arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Xiaoyi Zeng · 2026-09-07 07:44

EAGER：电商搜索中基于点击项的富化与对齐生成式查询推荐

E-commerce platforms increasingly display clickable query suggestions alongside items in the user feed, enabling users to refine or expand their intent without manually reformulating queries. Existing approaches either mine suggestions from historical logs -- limited to past beha…

本文如何被排名

报道来源 [1]

EAGER：电商搜索中基于点击项的富化与对齐生成式查询推荐

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