INTELLECTUAL ANALYSIS OF USER BEHAVIOR IN DIGITAL SERVICES

The article explores the application of machine learning techniques to evaluate user activity in digital products, focusing on classification and clustering models such as Long Short-Term Memory (LSTM), XGBoost, and KMeans. As digital platforms increasingly rely on user behavior analysis to drive personalization, engagement, and retention, the ability to interpret and predict user activity patterns has become essential for product and marketing strategies. This research analyzes current advancements (2022–2025) in behavioral analytics using artificial intelligence and presents a comparative study of model performance and explainability. The study implements an experimental pipeline incorporating data preprocessing, feature engineering, and model evaluation on a synthetic dataset emulating real-world digital interaction. The LSTM model is employed for temporal sequence analysis, aiming to detect patterns in user sessions and navigation paths. XGBoost is used for robust classification of user segments based on engagement metrics, while KMeans clustering enables the discovery of hidden user groups. Key visualization tools include Receiver Operating Characteristic (ROC) curves for classification accuracy, SHAP (SHapley Additive exPlanations) plots for feature importance, and 2D PCA projections for cluster analysis. The LSTM model demonstrated moderate predictive power but struggled with overfitting on small datasets. XGBoost achieved the highest ROC-AUC scores (>0.91), offering explainable and scalable results. The SHAP analysis revealed that session duration, frequency of return visits, and specific feature usage were the most influential factors in classification tasks. KMeans clustering, validated using silhouette scores, identified three distinct user clusters, enabling targeted personalization strategies. Challenges in the experiment included limited labeled data, feature sparsity, and model interpretability. Proposed solutions involve synthetic data generation, dimensionality reduction, and hybrid model architectures. The article concludes that machine learning significantly enhances user analytics by uncovering actionable patterns in digital behavior. However, model deployment requires careful consideration of data quality, scalability, and ethical aspects of user profiling. This research contributes to the growing field of behavioral analytics in human–computer interaction and supports data-driven decision-making in product management. Future directions include real-time user prediction engines, reinforcement learning for adaptive interfaces, and integration with A/B testing platforms.