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通过意图覆盖和查询级信用分配的生成查询建议Generative Query Suggestion via Intent Coverage and Query-Level Credit Assignment

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以下内容为 AI 自动翻译，英文原文附后供参考。

摘要

最近的研究提出了一种新颖的生成查询建议框架，旨在增强搜索环境中的用户参与度。该框架专注于预测用户意图并推荐相关的后续查询，通过双阶段优化过程实现。这种创新方法不仅改善了意图覆盖，还提高了建议查询的质量，在广泛测试中显著提高了点击率。研究结果表明，理解用户意图对于开发有效的查询建议系统至关重要。

Recent research has introduced a novel framework for generative query suggestion aimed at enhancing user engagement in search environments. The framework focuses on anticipating user intents and recommending relevant follow-up queries, which is achieved through a dual-stage optimization process. This innovative approach not only improves intent coverage but also elevates the quality of suggested queries, resulting in significantly higher click-through rates during extensive testing. The findings suggest that understanding user intent is crucial for developing effective query suggestion systems.

要点

  1. 开发了一种新的生成查询建议框架。

    A new framework for generative query suggestion has been developed.

  2. 该框架利用双阶段优化来增强意图覆盖和查询质量。

    The framework utilizes dual-stage optimization to enhance intent coverage and query quality.

  3. 测试表明，新方法导致用户参与度和点击率的提高。

    Testing indicates that the new method leads to increased user engagement and click-through rates.

为何重要

这项研究具有重要意义，因为它解决了提高搜索系统中用户参与度的关键挑战，这对用户满意度和商业成功至关重要。

This research is significant as it addresses the critical challenge of improving user engagement in search systems, which is essential for both user satisfaction and business success.

按谁的角度看

产品经理

对于产品经理而言，该框架提供了提升搜索功能用户体验的机会，可能导致更高的用户留存率和转化率。

For product managers, this framework presents an opportunity to enhance user experience in search functionalities, potentially leading to higher retention and conversion rates.

开发者

开发人员可以利用这些发现实施更复杂的查询建议算法，更好地理解和预测用户意图，从而提高整体系统性能。

Developers can leverage these findings to implement more sophisticated query suggestion algorithms that better understand and predict user intent, improving overall system performance.

学生与求职者

在技术领域的学生和求职者可以获得前沿研究的见解，这可能为未来的项目或职业道路提供参考，尤其是在人工智能和用户体验设计方面。

Students and job-seekers in the tech field can gain insights into cutting-edge research that could inform future projects or career paths in AI and user experience design.

覆盖来源

来源标题与摘录保留原文，以便核对与追溯。

  • arXiv cs.LG

    Generative Query Suggestion via Intent Coverage and Query-Level Credit Assignment

    Researchers developed a new framework for generative query suggestion that enhances user engagement by anticipating user intents and recommending relevant follow-up queries. The method employs dual-stage optimization to improve intent coverage and query quality, leading to increased click-through rates based on extensive testing.