Problem
Research questions and friction points this paper is trying to address.
User Experience
Multimodal
Simulation
Automated Testing
Actionable Insights
Automatic multimodal UX improvement recommendations from LLM agent user simulations
🤖 AI Summary
该研究使用AMUSER框架通过模拟用户行为自动生成多模态UX改进建议，解决了传统UX测试成本高、主观性强且难以扩展的问题。
📝 Abstract
Evaluating user experience (UX) on live websites through user testing is expensive, subjective, and difficult to scale. LLM agents offer a promising route to automating UX testing by simulating realistic user behaviour. However, existing simulation approaches typically lack multimodality and require time-consuming manual review to extract actionable insights. We formalise UX improvement recommendation from simulation data as a structured natural language generation and ranking problem, and establish an evaluation protocol using expert annotation and LLM-as-a-Judge. We present AMUSER, a multimodal framework which simulates user behaviour and automatically generates prioritised UX improvement recommendations from resulting data. We evaluate AMUSER on commercial websites and show that its recommendations substantially outperform those from text-only simulation (NDCG@3 = 0.758 versus 0.359) at an 89% lower simulation cost. Our results suggest an asymmetric role of multimodality: visual access during simulation improves recommendations through richer traces, while providing visual inputs during recommendation generation can modestly degrade quality. We also discuss practical deployment lessons from applying AMUSER to commercial websites.
Innovation
Methods, ideas, or system contributions that make the work stand out.
multimodal framework
UX improvement recommendations
automatic generation
visual access
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