In this paper, we introduce the novel task of Open-domain Urban Itinerary Planning (OUIP), a paradigm designed to generate personalized urban itineraries from user requests articulated in natural language. This approach is different from traditional itinerary planning, which often restricts the granularity of user inputs, thus hindering genuine personalization. To this end, we present ItiNera, an OUIP system that synergizes spatial optimization with large language models (LLMs) to provide services that customize urban itineraries based on users' needs. Upon receiving the user's itinerary request, the LLM first decomposes it into detailed components, identifying key requirements, including preferences and dislikes. Then, we use these specifics to select candidate POIs from a large-scale collection using embedding-based Preference-aware POI Retrieval. Finally, a preference score-based Cluster-aware Spatial Optimization module clusters, filters, and orders these POIs, followed by the LLM for detailed POI selection and organization to craft a personalized, spatially coherent itinerary. Moreover, we created an LLM-based pipeline to update and personalize a user-owned POI database. This ensures up-to-date POI information, supports itinerary planning, pre-trip research, POI collection, recommendations, and more. To the best of our knowledge, this study marks the first integration of LLMs to innovate itinerary planning, with potential extensions for various urban travel and exploration activities. Offline and online evaluations demonstrate the capacity of our system to deliver more responsive, personalized, and spatially coherent itineraries than current solutions. Our system, deployed on an online platform, has attracted thousands of users for their urban travel planning.

翻译：本文提出了开放域城市行程规划这一新颖任务范式，旨在根据用户以自然语言表述的请求生成个性化城市行程。该方法区别于传统行程规划——后者通常限制用户输入的细粒度，从而阻碍了真正的个性化。为此，我们提出了ItiNera系统，一个将空间优化与大语言模型协同融合的开放域行程规划系统，以提供基于用户需求定制城市行程的服务。在接收到用户的行程请求后，大语言模型首先将其分解为详细组成部分，识别出包括偏好与厌恶在内的关键需求。随后，我们利用这些具体信息，通过基于嵌入的偏好感知兴趣点检索方法，从大规模兴趣点集合中筛选候选点。接着，一个基于偏好得分的集群感知空间优化模块对这些兴趣点进行聚类、筛选与排序，再由大语言模型进行详细的兴趣点选择与组织，从而构建出个性化且空间连贯的行程。此外，我们创建了一个基于大语言模型的流程，用于更新并个性化用户自有的兴趣点数据库。这确保了兴趣点信息的时效性，并支持行程规划、行前研究、兴趣点收藏、推荐等多种功能。据我们所知，本研究首次实现了大语言模型在行程规划领域的创新性融合，并具备扩展到各类城市旅行与探索活动的潜力。离线与在线评估表明，相较于现有解决方案，我们的系统能够提供响应更迅速、更个性化且空间更连贯的行程。部署于在线平台的系统已吸引了数千用户用于其城市旅行规划。