User Behavior

User behavior modeling aims to understand and predict how individuals interact with systems, primarily to improve personalization and engagement. Current research focuses on developing sophisticated models, such as deep learning architectures (including transformers, LSTMs, and attention mechanisms), reinforcement learning frameworks, and causal inference methods, to capture complex, long-term user patterns across diverse data modalities (text, clicks, time series). These advancements have significant implications for various applications, including recommender systems, personalized advertising, and the design of more effective human-computer interfaces, particularly in e-commerce and social media.