Personal Travel Solver: A Preference-Driven LLM-Solver System for Travel Planning
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.
Citation:
@inproceedings{shao2025,
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},
booktitle = {The 63rd Annual Meeting of the Association for Computational Linguistics},
year = {2025}
}
Zijian Shao
邵子健
Jiancan Wu
吴剑灿 副研究员
