Abstract

Adaptive user behavior analysis involves understanding and anticipating how users interact with digital systems over time. With the explosion of data from web, mobile, and IoT systems, traditional static models are insufficient to capture temporal patterns and dynamic changes in user behavior. Sequence learning techniques—such as Hidden Markov Models (HMMs), Long Short‑Term Memory networks (LSTMs), Recurrent Neural Networks (RNNs), and Conditional Random Fields (CRFs)—have emerged as powerful tools for modeling sequential dependencies in user actions. This study examines the role of sequence learning in adaptive user behavior prediction, proposing a methodological framework that integrates data preprocessing, feature extraction, temporal modeling, and evaluation metrics. We review prior work on sequence learning approaches, outline a research methodology for building adaptive behavior models, and demonstrate how these models improve prediction accuracy compared to non‑sequential baselines. Key advantages include the ability to capture long‑range dependencies and adapt to evolving patterns, while disadvantages center on computational cost and data sparsity. Our results indicate that hybrid models combining classical sequence models with deep learning yield robust performance in both short‑term prediction and long‑term behavior modeling. Future work is directed toward scalability, interpretability, and ethical considerations.