Lost in Documentation? Let Our Docs Recommendation System Guide You Along!

Exploring the challenges of navigating complex documentation, this blog post introduces a Docs Recommendation System designed to streamline the process by suggesting the next best page to visit. The system utilizes a combination of algorithms, including TF-IDF for keyword extraction, node2vec for node embeddings, and PageRank for determining page influence. The setup involves a backend built with Python Flask and a frontend with React, supported by the MAGE graph library, GQLAlchemy, Docker, and Memgraph Lab for visualizations. The recommendation engine operates by scraping documentation content, extracting and cleaning text, and building a graph database to establish relationships between pages using similarity measures. Through graph algorithms, the system provides recommendations based on cosine similarity and adjacency matrices, while also offering insights into the most influential pages using PageRank. The post encourages readers to experiment with the system, highlighting its potential to enhance user experience by efficiently reducing search time within documentation.