Interactive Recommender System

Interactive recommender systems aim to improve recommendation accuracy and user satisfaction by incorporating real-time user feedback and preferences. Current research focuses on addressing biases in existing models, developing more effective preference elicitation techniques (including those using natural language and soft attributes), and leveraging advanced architectures like large language models and graph convolutional networks to enhance personalization and interaction. This field is significant because it strives to create more engaging and effective recommendation experiences, impacting various applications from e-commerce to personalized news feeds, while also advancing our understanding of user behavior and preference dynamics.