Leveraging LLMs to Create a Haptic Devices' Recommendation System

TL;DR

This paper introduces a novel LLM-based system that automates the creation of a haptic device database and provides accurate, user-satisfying recommendations for Grounded Force Feedback devices, improving accessibility and usability.

Contribution

It presents a new approach using LLMs to automate haptic device database creation and develop a recommendation system with dynamic retrieval methods.

Findings

Ranks in the top 10% of UEQ categories

Outperforms existing haptic search tools

No significant bias across user groups

Abstract

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.