@inproceedings{shao-etal-2025-personal,
    title = "Personal Travel Solver: A Preference-Driven {LLM}-Solver System for Travel Planning",
    author = "Shao, Zijian  and
      Wu, Jiancan  and
      Chen, Weijian  and
      Wang, Xiang",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.1339/",
    pages = "27622--27642",
    ISBN = "979-8-89176-251-0",
    abstract = "Personal travel planning is a challenging task that aims to find a feasible plan that not only satisfies diverse constraints but also meets the demands of the user{'}s explicit and implicit preferences. In this paper, we study how to integrate the user{'}s implicit preference into the progress of travel planning. We introduce RealTravel, an augmented version of the TravelPlanner by incorporating real user reviews and point-of-interest metadata from Google Local. Based on RealTravel, we propose Personal Travel Solver (PTS), an integrated system that combines LLMs with numerical solvers to generate travel plans that satisfy both explicit constraints and implicit user preferences. PTS employs a novel architecture that seamlessly connects explicit constraint validation with implicit preference modeling through five specialized modules. The experimental results demonstrate the system{'}s effectiveness, achieving better performance than baseline methods, and improvement in the level of personalization. Our data and code are available at [PersonalTravelSolver](https://github.com/cliftclift/PTS)."
}