User Satisfaction

User satisfaction research aims to understand and predict how users perceive and respond to systems, products, or services, ultimately seeking to optimize design for improved experiences. Current research focuses on developing accurate and interpretable models, often employing machine learning techniques like reinforcement learning, contextual bandits, and large language models, to analyze diverse data sources including user feedback, interaction logs, and contextual factors. These advancements have significant implications for improving the design of various systems, from conversational AI and recommender systems to autonomous vehicles and online platforms, leading to more user-centered and effective technologies.