标题： 利用大语言模型创建触觉设备推荐系统 显示英文标题

标题： Leveraging LLMs to Create a Haptic Devices' Recommendation System

摘要： 触觉技术已取得显著发展，但对现有触觉设备设计知识的认识不足阻碍了其发展。 本文通过利用大型语言模型（LLMs）的进步来开发一个触觉代理，特别关注于基于场景的力反馈（GFF）设备推荐。 我们的方法涉及使用来自研究论文和产品规格的信息自动化创建结构化的触觉设备数据库。 该数据库可根据用户查询推荐相关的GFF设备。 为了确保精确且上下文相关的推荐，系统采用了一种结合条件和语义搜索的动态检索方法。 与现有的UEQ和触觉设备搜索工具进行基准比较，所提出的触觉推荐代理在所有UEQ类别中排名前10%，在几乎所有子量表上均显示出平均差异的优势，并在不同用户群体中保持无显著性能偏差，展示了卓越的可用性和用户满意度。 显示英文摘要

摘要： Haptic technology has seen significant growth, yet a lack of awareness of existing haptic device design knowledge hinders development. This paper addresses these limitations by leveraging advancements in Large Language Models (LLMs) to develop a haptic agent, focusing specifically on Grounded Force Feedback (GFF) devices recommendation. Our approach involves automating the creation of a structured haptic device database using information from research papers and product specifications. This database enables the recommendation of relevant GFF devices based on user queries. To ensure precise and contextually relevant recommendations, the system employs a dynamic retrieval method that combines both conditional and semantic searches. Benchmarking against the established UEQ and existing haptic device searching tools, the proposed haptic recommendation agent ranks in the top 10\% across all UEQ categories with mean differences favoring the agent in nearly all subscales, and maintains no significant performance bias across different user groups, showcasing superior usability and user satisfaction.