Addressing itinerary modification is crucial for enhancing the travel experience as it is a frequent requirement during traveling. However, existing research mainly focuses on fixed itinerary planning, leaving modification underexplored due to the scarcity of need-to-modify itinerary data. To bridge this gap, we formally define the itinerary modification task and propose a general pipeline to construct the corresponding dataset, namely iTIMO. This pipeline frames the generation of need-to-modify itinerary data as an intent-driven perturbation task. It instructs large language models to perturb real-world itineraries using three operations: REPLACE, ADD, and DELETE. Each perturbation is grounded in three intents: disruptions of popularity, spatial distance, and category diversity. Furthermore, hybrid evaluation metrics are introduced to ensure perturbation effectiveness. We conduct comprehensive benchmarking on iTIMO to analyze the capabilities and limitations of state-of-the-art LLMs. Overall, iTIMO provides a comprehensive testbed for the modification task, and empowers the evolution of traditional travel recommender systems into adaptive frameworks capable of handling dynamic travel needs. Dataset, code and supplementary materials are available at https://github.com/zelo2/iTIMO.

翻译：行程修改是旅行过程中的常见需求，解决该问题对于提升旅行体验至关重要。然而，现有研究主要集中于固定行程规划，由于缺乏需修改的行程数据，修改任务尚未得到充分探索。为填补这一空白，我们正式定义了行程修改任务，并提出一种通用流程来构建相应数据集，即iTIMO。该流程将需修改行程数据的生成构建为意图驱动的扰动任务，通过指导大语言模型使用三种操作（替换、添加、删除）对真实行程进行扰动。每次扰动均基于三种意图：热门度干扰、空间距离干扰和类别多样性干扰。此外，我们引入了混合评估指标以确保扰动有效性。我们在iTIMO上进行了全面基准测试，以分析前沿大语言模型的能力与局限性。总体而言，iTIMO为行程修改任务提供了全面的测试平台，并推动传统旅行推荐系统向能够处理动态旅行需求的自适应框架演进。数据集、代码及补充材料可通过 https://github.com/zelo2/iTIMO 获取。