ITINERA：将空间优化与大语言模型集成以实现开放域城市行程规划
ITINERA: Integrating Spatial Optimization with Large Language Models for Open-domain Urban Itinerary Planning
AI总结：
该研究提出开放域城市行程规划任务OUIP，并构建融合大语言模型与簇感知空间优化的ITINERA系统，以从自然语言请求生成个性化、空间连贯的城市行程。
摘要：
Citywalk（城市漫步）作为近期流行的城市旅行形式，与传统行程规划相比，需要真正的个性化以及对细粒度请求的理解。本文提出开放域城市行程规划（OUIP）这一新任务，旨在根据自然语言形式的用户请求生成个性化城市行程。随后，我们提出ITINERA，这是一个将空间优化与大语言模型相结合的OUIP系统，可根据用户需求提供定制化城市行程。该过程包括分解用户请求、选择候选兴趣点（POI）、基于簇感知空间优化对POI进行排序，以及生成行程。在真实世界数据集上的实验以及已部署系统的表现表明，与现有解决方案相比，我们的系统能够提供个性化且空间连贯的行程。ITINERA的源代码可在https://github.com/YihongT/ITINERA获取。
英文摘要：
Citywalk, a recently popular form of urban travel, requires genuine personalization and understanding of fine-grained requests compared to traditional itinerary planning. In this paper, we introduce the novel task of Open-domain Urban Itinerary Planning (OUIP), which generates personalized urban itineraries from user requests in natural language. We then present ITINERA, an OUIP system that integrates spatial optimization with large language models to provide customized urban itineraries based on user needs. This involves decomposing user requests, selecting candidate points of interest (POIs), ordering the POIs based on cluster-aware spatial optimization, and generating the itinerary. Experiments on real-world datasets and the performance of the deployed system demonstrate our system's capacity to deliver personalized and spatially coherent itineraries compared to current solutions. Source codes of ITINERA are available at https://github.com/YihongT/ITINERA.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
