This project aims to build a game recommendation system for Steam users using machine learning techniques. It utilizes a custom dataset of Steam IDs to retrieve user-specific information such as owned games, playtimes, and game tags through the Steam Web API. The collected data is then processed and used to train a machine learning model.
By analyzing user preferences, playtimes, and game attributes like tags, the model learns patterns and relationships to generate personalized game recommendations. The recommendation system helps users discover new games tailored to their interests based on their existing game library.
- Data collection and processing using the Steam Web API
- User-specific information extraction, including owned games, playtimes, and game tags
- Machine learning model training to learn user preferences
- Personalized game recommendations based on user profiles
- Integration of the recommendation system into applications or services for user interaction