Interest Preference

Interest preference modeling aims to understand and predict individual preferences to personalize experiences and recommendations across various applications, from chatbots to e-commerce. Current research focuses on developing methods that dynamically adapt to diverse and evolving preferences, often employing techniques like multi-turn conversational interaction, in-context learning, and heterogeneous hypergraph networks to capture complex relationships between user behavior and item attributes, including price. These advancements have significant implications for improving user experience in personalized systems and for developing fairer and more effective machine learning models.