User Behavior Modeling

User behavior modeling aims to understand and predict user actions based on their historical interactions, primarily to improve recommendation systems and personalized experiences. Current research heavily focuses on efficiently handling extremely long user sequences, employing techniques like hierarchical clustering, two-stage retrieval methods, and novel attention mechanisms within transformer-based models to capture complex temporal dependencies. These advancements are crucial for large-scale applications, enabling more accurate and personalized recommendations for hundreds of millions of users in real-time, as demonstrated by successful industrial deployments.