Deconstructing Taste: Toward a Human-Centered AI Framework for Modeling Consumer Aesthetic Perceptions

2026-01-23

AI Summary

This study addresses a central challenge in industrial design: effectively translating consumers’ abstract and subjective aesthetic preferences—such as “sportiness”—into actionable design guidance. To this end, the authors propose a human-centered computing framework that, for the first time, integrates subjective evaluations from both consumers and designers, domain-specific design features (e.g., wheel spoke configurations), and objective visual metrics extracted via computer vision (e.g., texture). By jointly modeling these multi-source features, the approach constructs an interpretable aesthetic perception model that explicitly links aesthetic judgments to concrete design elements. This enables product teams to accurately anticipate user preferences early in the design process, facilitating efficient exploration and critical iteration of design alternatives.

Technology Category

• Knowledge Representation and Reasoning: Preferences
• Computer Vision: Visual Reasoning & Symbolic Representations
• Cognitive Modeling & Cognitive Systems: Affective Computing

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Economics, Online Markets and Human Computation: Economic aspects and information design of Web, GenAI, and cloud computing
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Understanding and modeling consumers'stylistic taste such as"sporty"is crucial for creating designs that truly connect with target audiences. However, capturing taste during the design process remains challenging because taste is abstract and subjective, and preference data alone provides limited guidance for concrete design decisions. This paper proposes an integrated human-centered computational framework that links subjective evaluations (e.g., perceived luxury of car wheels) with domain-specific features (e.g., spoke configuration) and computer vision-based measures (e.g., texture). By jointly modeling human-derived (consumer and designer) and machine-extracted features, our framework advances aesthetic assessment by explicitly linking model outcomes to interpretable design features. In particular, it demonstrates how perceptual features, domain-specific design patterns, and consumers'own interpretations of style contribute to aesthetic evaluations. This framework will enable product teams to better understand, communicate, and critique aesthetic decisions, supporting improved anticipation of consumer taste and more informed exploration of design alternatives at design time.

Problem

• aesthetic perception
• consumer taste
• design modeling
• subjective evaluation
• human-centered AI

Innovation

• human-centered AI
• aesthetic perception
• computational design
• computer vision
• design interpretation