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

As web environments become increasingly complex and dynamic, traditional machine learning models often fail to capture the semantic relationships between heterogeneous data sources or adapt to evolving user preferences in real time. This paper investigates how to build a more robust, context-aware system for web enhancement—specifically for tasks like content recommendation, navigation optimization, and service adaptation—by integrating semantic graph modeling with adaptive reinforcement learning.

The authors propose the Multi-Granular Attention-based Reinforcement Web Intelligent Enhancement System (MGAR-WIES). The framework operates in three primary stages:

1. Semantic Graph Modeling: The system collects heterogeneous web data (structured, semi-structured, and unstructured) and transforms it into a dynamic semantic graph. It uses graph attention networks (GATs) to generate embeddings that capture both local relevance and global contextual dependencies between entities.
2. Multi-Agent Reinforcement Learning (MARL): The system employs a multi-agent reinforcement learning strategy where different agents manage distinct web services. These agents use the graph-based semantic states as inputs to optimize personalized actions, reducing state-space complexity and accelerating policy convergence.
3. Continuous Online Adaptation: To handle concept drift and changing user behavior, the framework includes a feedback loop that updates both the graph representations and the reinforcement learning policies in real time based on user interactions.

The proposed MGAR-WIES framework demonstrates improved performance in web intelligence tasks, achieving an accuracy of 80% compared to existing baseline approaches. By utilizing attention-driven graph embeddings, the system effectively reduces the complexity of the state space, allowing the reinforcement learning agents to make more stable and contextually grounded decisions in dynamic environments.