As a lay user creates an art piece using an interactive generative art tool, what, if anything, do the choices they make tell us about them and their preferences? These preferences could be in the specific generative art form (e.g., color palettes, density of the piece, thickness or curvatures of any lines in the piece); predicting them could lead to a smarter interactive tool. Or they could be preferences in other walks of life (e.g., music, fashion, food, interior design, paintings) or attributes of the person (e.g., personality type, gender, artistic inclinations); predicting them could lead to improved personalized recommendations for products or experiences.
To study this research question, we collect preferences from 311 subjects, both in a specific generative art form and in other walks of life. We analyze the preferences and train machine learning models to predict a subset of preferences from the remaining. We find that preferences in the generative art form we studied cannot predict preferences in other walks of life better than chance (and vice versa). However, preferences within the generative art form are reliably predictive of each other.
Scale over Preference: The Impact of AI-Generated Content on Online Content Ecology
University of Science and Technology of China（中国科学技术大学）；National University of Singapore（新加坡国立大学）；Kuaishou Technology（快手科技）；The Chinese University of Hong Kong（香港中文大学）
Dynamic Detection of Relevant Objectives and Adaptation to Preference Drifts in Interactive Evolutionary Multi-Objective Optimization
Centre for Logistics & Sustainability Analytics, Kent Business School, University of Kent（肯特大学肯特商学院物流与可持续性分析中心）；Université de Haute-Alsace（上阿尔萨斯大学）；Alliance Manchester Business School, University of Manchester（曼彻斯特大学联盟曼彻斯特商学院）
Prompt-Driven Agentic Video Editing System: Autonomous Comprehension of Long-Form, Story-Driven Media
University of British Columbia（不列颠哥伦比亚大学）；University of Bristol（布里斯托大学）；Department of Engineering, University of Cambridge（剑桥大学工程系）；School of Computer Science, University of Bristol（布里斯托大学计算机科学学院）
Fairness Begins with State: Purifying Latent Preferences for Hierarchical Reinforcement Learning in Interactive Recommendation
Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences（中国科学院重庆绿色智能技术研究所）；Chongqing School, University of Chinese Academy of Sciences（中国科学院大学重庆学院）；The First Affiliated Hospital, University of Science and Technology of China（中国科学技术大学第一附属医院）；City University of Hong Kong（香港城市大学）
