ARCHITECTURE OF INFORMATION TECHNOLOGY FOR IMPLEMENTING AN AUTONOMOUS ADAPTIVE CONTENT GENERATION SYSTEM BASED ON MACHINE LEARNING METHODS

Abstract

This paper presents the architecture of an information technology system for autonomous adaptive content generation based on machine learning methods. The main problem addressed is ensuring effective dynamic content selection according to user behavior, which is critical for information systems, online platforms, and computer games. To solve this problem, a modular architecture is proposed, including: a data collection module for obtaining and preprocessing user information, an analytical module for predicting user responses using LSTM models and neural network agents, a content generation module based on Generative Adversarial Networks (GAN), and a control module implementing user interaction strategy optimization through reinforcement learning (RL). The system algorithm is described in detail, implementing a full cycle of autonomous adaptation: data collection, prediction, content generation, user response evaluation, model parameter update. Training was performed, the results of which showed a gradual decrease in the loss function (MSE). A comparative analysis of the full LSTM + GAN + RL system with baseline models (LSTM without generator, GAN without RL) was conducted. The comparative results indicate a significant reduction in mean squared error, improved prediction accuracy, and increased user engagement, confirming the effectiveness of the proposed modular architecture. The study demonstrates the practical applicability of autonomous adaptive content generation for information systems, online platforms, and computer games, while also highlighting future perspectives for improving generative and adaptive algorithms combined with multi-criteria reinforcement learning