Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
7-2026
Abstract
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 shape 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 shape 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 metrics are proposed 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.
Keywords
Travel Recommender Systems, Large Language Model, Travel ItineraryModification, Synthetic Data Generation
Discipline
Artificial Intelligence and Robotics | Databases and Information Systems
Research Areas
Intelligent Systems and Optimization
Areas of Excellence
Digital transformation
Publication
SIGIR '26: Proceedings of the 49th International ACM SIGIR Conference on Research and Development in Information Retrieval, Melbourne, Australia, July 20-24
First Page
3235
Last Page
3243
Identifier
10.1145/3805712.3808576
Publisher
ACM
City or Country
New York
Citation
HUANG, Zhuoxuan; MA, Yunshan; ZHANG, Hongyu; MA, Hua; and SUN, Zhu.
iTIMO: An LLM-empowered synthesis dataset for travel itinerary modification. (2026). SIGIR '26: Proceedings of the 49th International ACM SIGIR Conference on Research and Development in Information Retrieval, Melbourne, Australia, July 20-24. 3235-3243.
Available at: https://ink.library.smu.edu.sg/sis_research/11270
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/3805712.3808576