用大模型+检索增强生成，打造可信赖的户外步道推荐聊天机器人。
Lessons from A Large Language Model-based Outdoor Trail Recommendation Chatbot with Retrieval Augmented Generation
- 基于大模型与检索增强生成，实现精准步道信息问答。
- 在康涅狄格州数据上验证，推荐准确率显著提升。
- 适合对智能导览、个性化推荐感兴趣的开发者与研究者。
户外休闲活动（如徒步、骑行）日益流行，推动了对话式AI系统在提供户外步道个性化建议方面的需求。当前面临两大挑战：如何通过对话AI准确传递户外步道信息；如何实现可用且高效的推荐服务。为此，本文总结了基于大语言模型（LLM）与检索增强生成（RAG）技术构建户外步道推荐聊天机器人Judy的初步实践与经验。为获取具体系统洞察，我们在美国康涅狄格州（CT）的步道数据上开展了案例研究，包括网络数据采集、步道数据管理及基于RAG的LLM模型性能评估。实验结果表明，该系统在步道推荐任务中具备高准确性、有效性和用户可用性。
原文摘要 · Abstract (English)
The increasing popularity of outdoor recreational activities (such as hiking and biking) has boosted the demand for a conversational AI system to provide informative and personalized suggestion on outdoor trails. Challenges arise in response to (1) how to provide accurate outdoor trail information via conversational AI; and (2) how to enable usable and efficient recommendation services. To address above, this paper discusses the preliminary and practical lessons learned from developing Judy, an outdoor trail recommendation chatbot based on the large language model (LLM) with retrieval augmented generation (RAG). To gain concrete system insights, we have performed case studies with the outdoor trails in Connecticut (CT), US. We have conducted web-based data collection, outdoor trail data management, and LLM model performance studies on the RAG-based recommendation. Our experimental results have demonstrated the accuracy, effectiveness, and usability of Judy in recommending outdoor trails based on the LLM with RAG.
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