构建旅行行程修改数据集，让智能系统学会动态调整行程。
iTIMO: An LLM-empowered Synthesis Dataset for Travel Itinerary Modification
- 用大模型按三种意图扰动真实行程，生成修改数据
- 通过替换、增加、删除操作实现行程修改，覆盖热门度、距离、类别多样性
- 提供评测指标和基准测试，助力自适应推荐系统发展
行程修改对提升旅行体验至关重要，但现有研究多聚焦于固定行程规划，因缺乏需要修改的行程数据，导致修改任务未被充分探索。为此，我们正式定义了行程修改任务，提出通用数据集构建流程 iTIMO。该流程将生成需修改行程数据视为意图驱动的扰动任务，指导大语言模型使用替换、添加、删除三种操作，基于热门度、空间距离、类别多样性三类意图对真实行程进行扰动。同时引入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，分析当前主流大模型在该任务上的能力与局限。总体而言，iTIMO 为行程修改任务提供了完整测试平台，推动传统推荐系统向能应对动态需求的自适应框架演进。数据集、代码及补充材料已开源：https://github.com/zelo2/iTIMO。
原文摘要 · Abstract (English)
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.
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