METHODOLOGY FOR DEVELOPING AN ADAPTIVE CONTENT GENERATION SYSTEM

Abstract

Purpose. The aim of this article is to develop a methodology for creating an adaptive content generation system that ensures a high level of personalization, scalability, and integration of modern machine learning algorithms. Special attention is given to staged data processing through ETL pipelines, real-time stream processing with 1-second micro-batches, and the integration of ML models for dynamic content adaptation based on user behavior.

Methodology. This study proposes a combined approach that unites ETL processes for data preparation and normalization, stream processing to ensure real-time performance and minimal latency, and the integration of ML models with incremental updates for content prediction and personalization. The methodology includes a formal system representation through functional blocks, mathematical data processing models, and modern frameworks for scalable computation, enabling flexible management of large event streams and diverse user scenarios.

Findings. Experimental evaluation was conducted on a dataset containing 1,200,000 user interaction events, 48,000 unique users, and 12,500 content items over a 30-day period. Comparison between streaming and batch approaches demonstrated a 6-percentage-point increase in Accuracy, improvements in Precision, Recall, and F1-score, as well as more than a twofold reduction in Latency. Visual analysis and statistical validation confirmed the stability and predictability of the streaming architecture, indicating comprehensive improvements in both personalization quality and system performance.

Originality. A formalized model of an adaptive content generation system is proposed, integrating ETL processes, stream processing with micro-batches, and ML models within a unified architecture. Incremental model updating algorithms, dynamic load balancing, and real-time adaptive content adjustment mechanisms have been developed, which have not been described in current literature.

Practical value. The proposed methodology can be applied in recommendation systems, online learning platforms, games, and other digital services where content personalization and adaptability are critical. It reduces data processing costs, enhances user experience, ensures system stability and predictability, and facilitates the integration of modern machine learning algorithms into corporate solutions.