User Perception
User perception research investigates how individuals understand and interact with technology, aiming to optimize design for improved usability, trust, and satisfaction. Current research focuses on understanding the impact of various factors—including AI labeling, explanation formats, and cultural context—on user experience, employing models like reinforcement learning, large language models, and contextual bandits to personalize interactions and improve outcomes. This field is crucial for developing human-centered AI systems across diverse applications, from recommender systems and e-commerce to healthcare and education, ensuring technology aligns with user needs and expectations.
Papers
UOEP: User-Oriented Exploration Policy for Enhancing Long-Term User Experiences in Recommender Systems
Changshuo Zhang, Sirui Chen, Xiao Zhang, Sunhao Dai, Weijie Yu, Jun Xu
From User Surveys to Telemetry-Driven Agents: Exploring the Potential of Personalized Productivity Solutions
Subigya Nepal, Javier Hernandez, Talie Massachi, Kael Rowan, Judith Amores, Jina Suh, Gonzalo Ramos, Brian Houck, Shamsi T. Iqbal, Mary Czerwinski
One Agent Too Many: User Perspectives on Approaches to Multi-agent Conversational AI
Christopher Clarke, Karthik Krishnamurthy, Walter Talamonti, Yiping Kang, Lingjia Tang, Jason Mars
Singing the Body Electric: The Impact of Robot Embodiment on User Expectations
Nathaniel Dennler, Stefanos Nikolaidis, Maja Matarić