System Design Frameworks for Complex Intelligent Systems

Abstract

E-commerce recommendation environments demand high adaptive intelligence capable of modeling complex user–item interactions, contextual variations, and large-scale retrieval efficiency. Existing approaches often rely on isolated collaborative filtering or deep learning (DL) models that struggle with scalability and dynamic preference shifts. Most models also fail to integrate contextual signals and reinforcement-driven optimization simultaneously. A unified intelligent framework is designed to enhance recommendation accuracy, scalability, and adaptive decision-making in large-scale e-commerce environments. A Graph-Attentive Reinforced E-Commerce Retrieval Framework (GARERF) is introduced, integrating graph neural representation learning, attention-based neighbor weighting, Approximate Nearest Neighbor (ANN)-based retrieval efficiency, and reinforcement learning-driven ranking optimization. The framework combines Graph Convolutional Attention Network (GCAN) for embedding generation, followed by ANN retrieval for candidate selection, and Double Deep Q-Network (DDQN) for dynamic ranking optimization. Multi-source e-commerce interaction datasets consisting of 12,000 records, utilized user clicks, purchase history, ratings, and contextual behavioral logs. Min–Max normalization is applied to ensure data consistency. User–item embeddings are derived using graph-based propagation and attention-weighted neighborhood aggregation. The graph module learns relational structures, the retrieval module efficiently filters candidates, the reinforcement module optimizes ranking decisions through reward feedback, and the fusion module integrates contextual signals. Python, PyTorch, GCAN libraries, ANN indexing, and reinforcement learning toolkits were implemented in this research. Experimental results demonstrate a precision of 0.93, a recall of 0.85, and a Click-Through Rate (CTR) of 0.30. The integrated framework demonstrates strong adaptability, scalability, and intelligent decision capability for complex e-commerce environments.