Multi-Granular Attention-Driven Reinforcement Learning Framework for Web Intelligent Enhancement Systems

• URL: http://arxiv.org/abs/2606.19690v1
• Date: Thu, 18 Jun 2026 01:38:10 GMT
• Title: Multi-Granular Attention-Driven Reinforcement Learning Framework for Web Intelligent Enhancement Systems
• Abstract summary: MGAR-WIES is proposed to address the challenges by integrating semantic graph modeling, attention mechanisms, and adaptive reinforcement learning.<n>The proposed MGAR-WIES achieved better results in terms of accuracy (80%) when compared with existing approaches.
• Score: 3.4838923094386085
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: From the past few years, web intelligent enhancement systems increasingly rely on heterogeneous and dynamic web data to deliver personalized, context-aware services. However, traditional machine learning, deep learning, and reinforcement learning models often struggle with semantic understanding, adaptability, and scalability in continuously evolving web environments. In this research, a Multi-Granular Attention-based Reinforcement Web Intelligent Enhancement System (MGAR-WIES) is proposed to address the challenges by integrating semantic graph modeling, attention mechanisms, and adaptive reinforcement learning. Initially, heterogeneous web data comprising structured, semi-structured and unstructured sources are collected and preprocessed for generating unified feature representations. These representations are transformed into a dynamic semantic graph, where entities and their relationships are modeled by using graph embeddings enhanced by attention mechanisms for capturing both local relevance and global contextual dependencies. Subsequently, an adaptive multi-agent reinforcement learning strategy leverages the attention-aware semantic states to optimize personalized web actions like content recommendation, navigation optimization, and service adaptation. Finally, the continuous online feedback is further integrated to update graph representations and learning policies in real time by ensuring sustained adaptability and performance. The proposed MGAR-WIES acheived better results in terms of accuracy (80%) when compared with existing approaches.

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