Enhancing Cross-Domain Book Classification Through Caching-Enabled Networks and Transformer Technology

Abstract

Book classification is a crucial task for libraries and a fundamental aspect of their service offerings. Cross-domain book classification, in particular, presents significant challenges due to the diversity and complexity of content across different genres and subjects. To tackle these challenges, a user-oriented strategy employing Transformer network (TN) is developed to fulfill the need for superior content quality and classification. Our proposed method leverages the self-attention mechanism of TN for precise feature extraction and classification, combining it with principal component analysis to ensure a comprehensive understanding of book content. This integration represents a technical innovation that enhances the model's ability to handle diverse datasets with improved accuracy and robustness. Our approach merges TN with caching-enabled networks (CEN) to enhance accuracy and robustness. Driven by the necessity for improved cross-domain classification, our strategy aims to standardize book classifications, thus improving user satisfaction. The primary actions encompass improved classification management, feedback systems, and evaluation frameworks. This work highlights the innovative fusion of TN and CEN, showcasing how these advanced techniques can significantly elevate the performance of library classification systems. Our work demonstrates that high-quality book classification can significantly improve library services and user experience. Furthermore, it aligns with the broader applications of CEN in emerging networking technologies, showing the potential for cutting-edge techniques to revolutionize library services.