The text discusses the development of a metadata generation system using Stack Overflow tags and topic keywords. The system uses graph theory to analyze relationships between topics, allowing for automated page rankings and personalized recommendations for customers. To improve accuracy, an ontology is added to provide context, and the TF-IDF algorithm is used to balance term frequencies with commonness. The system also incorporates parsing of ebooks to extract keyword data and subsections, enabling a more accurate picture of content. A spreading activation algorithm is applied to customers' profiles, adding weights to topics based on their interests and activities. This allows for dynamic personalization of recommendations, including automatic page rankings and customer profiling. The system has been successfully implemented in production using Neo4j and Rails API, enabling graph-based search, modularized content, and skill mapping.