Navigating complex documentation can be challenging, but a Docs Recommendation System can streamline the process by suggesting the next pages to visit based on your current reading. This system utilizes a combination of algorithms such as TF-IDF, node2vec, and link prediction to analyze document content and provide recommendations, enhancing the efficiency of learning and exploration within documentation. The backend of the application is built using Python Flask, while the frontend employs React, with Docker facilitating deployment. The recommendation engine extracts and processes text from documentation using libraries like BeautifulSoup and jusText, and then uses TF-IDF to identify key terms for generating document vectors. These vectors are compared using cosine similarity to recommend similar documents. Additionally, Memgraph's graph database and algorithms enable the creation of a structured graph of documentation pages, allowing for advanced recommendations through node embeddings and predicting potential links between pages. The system also incorporates PageRank to assess the importance of pages within the documentation, providing a comprehensive tool for efficiently navigating and understanding large sets of documents.