English(EN) SCOUT: Supply-Aware Cold-Start Proactive Query Suggestion for Travel Search

新SCOUT框架改进旅行搜索查询建议

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-04 23:30

研究人员开发了SCOUT，一个旨在改进旅行搜索引擎中主动查询建议的新框架。与通用搜索不同，旅行搜索受到可用库存的限制，而传统的基于LLM的建议方法由于缺乏历史用户数据和自由文本查询而难以解决冷启动问题。SCOUT通过利用供应方系统反馈，特别是搜索引擎重排器提供的查询-列表匹配分数，来训练强化学习策略，从而解决这一问题。这种方法在不增加推理成本的情况下提高了库存匹配率和多样性，使得供应感知建议在实时旅行搜索中变得可行。 AI

影响通过解决冷启动和供应感知挑战，增强了LLM在旅行搜索等受限领域的应用。

排序理由详细介绍特定领域查询建议新框架的学术论文。[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; 详细介绍特定领域查询建议新框架的学术论文。[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: 3 days old; Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full 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) · Stephanie Moyerman · 2026-10-04 23:30

SCOUT：面向旅游搜索的供应感知冷启动主动查询建议

Generative query suggestion, powered by Large Language Models (LLMs), has become increasingly popular in search and conversational systems to reduce user friction and guide intent formulation. Existing approaches align suggestions with user preferences (e.g., clicks or conversion…

本文如何被排名

报道来源 [1]

SCOUT：面向旅游搜索的供应感知冷启动主动查询建议

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