TravelAgent: An AI Assistant for Personalized Travel Planning
Fuente:
arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866909313415512064 |
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| author | Chen, Aili Ge, Xuyang Fu, Ziquan Xiao, Yanghua Chen, Jiangjie |
| author_facet | Chen, Aili Ge, Xuyang Fu, Ziquan Xiao, Yanghua Chen, Jiangjie |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_08069 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | TravelAgent: An AI Assistant for Personalized Travel Planning Chen, Aili Ge, Xuyang Fu, Ziquan Xiao, Yanghua Chen, Jiangjie Artificial Intelligence Computation and Language 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. |
| title | TravelAgent: An AI Assistant for Personalized Travel Planning |
| topic | Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2409.08069 |