Building Video Game Recommender Systems with FastAPI, PostgreSQL, and Render: Part 1

A tutorial demonstrates building a video game recommendation system using PostgreSQL for data storage, FastAPI for API endpoints, and machine learning techniques to suggest games based on user preferences. The system stores game data and tags in a database, then calculates similarity scores between games using vectorized tag data. When users add games to their library through API requests, a background process generates personalized recommendations by comparing their liked games to the entire catalog using cosine similarity.