TravelAgent: An AI Assistant for Personalized Travel Planning
This addresses the need for automated, personalized travel planning services for users in the tourism domain, but it appears incremental as it builds on existing LLM-based methods.
The paper tackles the problem of creating practical and customized travel itineraries in dynamic scenarios by introducing TravelAgent, an AI assistant that uses large language models to achieve rationality, comprehensiveness, and personalization, with evaluation showing effectiveness in these criteria and accurate personalized recommendations.
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.