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English(EN) Embark Now: User Demand Oriented Framework for Multi-day Urban Travel Itinerary Planning

新的LLM框架优化城市旅行行程

研究人员开发了一个名为Embark Now的新框架,用于规划多日城市旅行行程。该框架集成了大型语言模型(LLMs)以精确捕捉用户偏好,并采用增强的贪婪随机自适应搜索过程(GRASP)算法来生成可行计划。在北京和天津的数据集上进行的实验表明,Embark Now的性能显著优于现有的最先进方法,行程得分提高了11%以上,并提高了计算效率。 AI

影响 该框架通过利用LLMs理解用户偏好,有望提高旅行规划应用程序中的用户满意度和效率。

排序理由 该集群包含一篇详细介绍新框架和实验结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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

新的LLM框架优化城市旅行行程

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该集群包含一篇详细介绍新框架和实验结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Rongbo Qi, Yaqi Zhang, Shijun Yan, Xuemeng Liu, Xiangrui Cai, Chunyao Song ·

    即刻启程:面向用户需求的多元城市旅行行程规划框架

    arXiv:2607.10651v1 Announce Type: new Abstract: In large urban areas, planning multi-day travel itineraries is challenging due to the abundance of Points of Interest (POIs), diverse user preferences, and constraints such as opening hours. Effective solutions must dynamically acco…