用大模型自动推荐触觉设备，提升设计效率与用户体验。
Leveraging LLMs to Create a Haptic Devices' Recommendation System
- 基于大模型构建结构化触觉设备数据库，支持精准检索。
- 在UEQ测评中综合表现位列前10%，多维度优于现有工具。
- 适合触觉交互设计师、产品开发者快速选型参考。
触觉技术发展迅速，但现有触觉设备设计知识缺乏有效认知，制约了进一步发展。本文利用大型语言模型（LLMs）构建触觉推荐代理，聚焦于地面力反馈（GFF）设备的推荐。通过自动化整合研究论文与产品规格信息，建立结构化触觉设备数据库，支持根据用户查询推荐相关GFF设备。为确保推荐的准确性和上下文相关性，系统采用结合条件搜索与语义搜索的动态检索方法。在与现有通用评估量表UEQ及触觉设备搜索工具的对比中，该推荐代理在所有UEQ类别中均位列前10%，多数子量表得分显著更优，且在不同用户群体间无显著性能偏差，展现出卓越的可用性与用户满意度。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。