用大模型打造能动态规划个性化行程的旅行助手
TravelAgent: An AI Assistant for Personalized Travel Planning
- 基于大模型分模块设计，支持动态场景下的智能行程生成
- 在合理、全面、个性三方面均优于现有系统
- 适合需要定制化旅行方案的用户或研究者参考
随着全球旅游业发展和人工智能技术进步，智能旅行规划服务成为重要研究方向。在多维度约束的动态真实旅行场景中，自动生成合理、全面且个性化的旅行行程需满足三个关键目标：合理性、全面性与个性化。然而，现有基于规则组合或大语言模型（LLM）的规划方法难以同时满足这些标准。为此，本文提出TravelAgent，一个由大语言模型驱动的旅行规划系统，旨在生成符合动态场景的合理、全面且个性化的行程。系统包含工具使用、推荐、规划与记忆四个模块。通过真人与模拟用户评估，验证了其在三项指标上的整体有效性，并确认个性化推荐的准确性。
原文摘要 · Abstract (English)
As global tourism expands and artificial intelligence technology advances, intelligent travel planning services have emerged as a significant research focus. Within dynamic real-world travel scenarios with multi-dimensional constraints, services that support users in automatically creating practical and customized travel itineraries must address three key objectives: Rationality, Comprehensiveness, and Personalization. However, existing systems with rule-based combinations or LLM-based planning methods struggle to fully satisfy these criteria. To overcome the challenges, we introduce TravelAgent, a travel planning system powered by large language models (LLMs) designed to provide reasonable, comprehensive, and personalized travel itineraries grounded in dynamic scenarios. TravelAgent comprises four modules: Tool-usage, Recommendation, Planning, and Memory Module. We evaluate TravelAgent's performance with human and simulated users, demonstrating its overall effectiveness in three criteria and confirming the accuracy of personalized recommendations.
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