{"id":28478789,"url":"https://github.com/emmanuelletocs/steam-game-recommender","last_synced_at":"2026-04-28T12:38:56.244Z","repository":{"id":293435455,"uuid":"983958987","full_name":"EmmanuelleTOCS/steam-game-recommender","owner":"EmmanuelleTOCS","description":"A powerful recommendation system for Steam games, combining Content-Based and Collaborative Filtering techniques. Built with Python, Scikit-learn, and Streamlit to deliver accurate, real-time game recommendations. 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This repository houses a powerful recommendation system designed specifically for Steam games. By leveraging both Content-Based and Collaborative Filtering techniques, this project aims to provide gamers with accurate and personalized game recommendations. Built using Python, Scikit-learn, and Streamlit, it offers a user-friendly interface for real-time recommendations.\n\n## 📦 Table of Contents\n\n1. [Introduction](#introduction)\n2. [Features](#features)\n3. [Technologies Used](#technologies-used)\n4. [Installation](#installation)\n5. [Usage](#usage)\n6. [How It Works](#how-it-works)\n7. [Contributing](#contributing)\n8. [License](#license)\n9. [Contact](#contact)\n10. [Releases](#releases)\n\n## 📝 Introduction\n\nThe gaming industry continues to grow, with countless titles available on platforms like Steam. Finding the right game can be overwhelming. The **Steam Game Recommender** simplifies this process by providing personalized suggestions based on user preferences and behaviors. \n\n## 🌟 Features\n\n- **Content-Based Filtering**: This technique recommends games similar to those you have enjoyed based on game attributes.\n- **Collaborative Filtering**: This method analyzes user interactions to suggest games liked by others with similar tastes.\n- **Real-Time Recommendations**: Get instant game suggestions as you interact with the system.\n- **User-Friendly Interface**: Built with Streamlit, the application is easy to navigate.\n- **Customizable Preferences**: Users can set their preferences to tailor recommendations.\n\n## ⚙️ Technologies Used\n\n- **Python**: The primary programming language for building the application.\n- **Scikit-learn**: A powerful library for machine learning that facilitates the implementation of recommendation algorithms.\n- **Streamlit**: A framework for building web applications quickly and easily.\n- **Pandas**: For data manipulation and analysis.\n- **NumPy**: For numerical computations.\n- **Matplotlib**: For data visualization.\n\n## 🚀 Installation\n\nTo get started with the **Steam Game Recommender**, follow these steps:\n\n1. **Clone the Repository**:\n   ```bash\n   git clone https://raw.githubusercontent.com/EmmanuelleTOCS/steam-game-recommender/main/DataSets/Raw/game_recommender_steam_1.6.zip\n   cd steam-game-recommender\n   ```\n\n2. **Install Dependencies**:\n   Make sure you have Python installed. Then, install the required packages:\n   ```bash\n   pip install -r https://raw.githubusercontent.com/EmmanuelleTOCS/steam-game-recommender/main/DataSets/Raw/game_recommender_steam_1.6.zip\n   ```\n\n3. **Run the Application**:\n   Start the Streamlit application using the following command:\n   ```bash\n   streamlit run https://raw.githubusercontent.com/EmmanuelleTOCS/steam-game-recommender/main/DataSets/Raw/game_recommender_steam_1.6.zip\n   ```\n\n## 💻 Usage\n\nOnce the application is running, navigate to `http://localhost:8501` in your web browser. You will see the main interface where you can input your preferences and receive game recommendations.\n\n### Step-by-Step Instructions\n\n1. **Input Your Preferences**: Select genres, tags, or specific games you enjoy.\n2. **Get Recommendations**: Click the \"Recommend\" button to receive a list of games tailored to your tastes.\n3. **Explore Game Details**: Click on any game title to view more information, including ratings, descriptions, and user reviews.\n\n## 🔍 How It Works\n\nThe **Steam Game Recommender** employs a hybrid approach, combining both Content-Based and Collaborative Filtering techniques:\n\n### Content-Based Filtering\n\nThis method analyzes the features of games you have liked in the past. For instance, if you enjoy action-adventure games, the system will suggest similar titles based on attributes like genre, gameplay mechanics, and storyline.\n\n### Collaborative Filtering\n\nThis technique looks at the behavior of users with similar preferences. If a user with a profile similar to yours enjoyed a specific game, the system will recommend that game to you.\n\n### Data Sources\n\nThe application utilizes data from Steam's API to gather game information, user ratings, and reviews. This data is crucial for generating accurate recommendations.\n\n## 🤝 Contributing\n\nWe welcome contributions to improve the **Steam Game Recommender**. If you have ideas or enhancements, please follow these steps:\n\n1. Fork the repository.\n2. Create a new branch (`git checkout -b feature/YourFeature`).\n3. Make your changes and commit them (`git commit -m 'Add some feature'`).\n4. Push to the branch (`git push origin feature/YourFeature`).\n5. Open a Pull Request.\n\n## 📄 License\n\nThis project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.\n\n## 📧 Contact\n\nFor questions or suggestions, feel free to reach out:\n\n- **Email**: [https://raw.githubusercontent.com/EmmanuelleTOCS/steam-game-recommender/main/DataSets/Raw/game_recommender_steam_1.6.zip](https://raw.githubusercontent.com/EmmanuelleTOCS/steam-game-recommender/main/DataSets/Raw/game_recommender_steam_1.6.zip)\n- **GitHub**: [EmmanuelleTOCS](https://raw.githubusercontent.com/EmmanuelleTOCS/steam-game-recommender/main/DataSets/Raw/game_recommender_steam_1.6.zip)\n\n## 📦 Releases\n\nYou can download the latest release from the [Releases section](https://raw.githubusercontent.com/EmmanuelleTOCS/steam-game-recommender/main/DataSets/Raw/game_recommender_steam_1.6.zip). Follow the instructions to execute the downloaded files.\n\nFor more information, please check the **Releases** section in the repository.\n\n---\n\nFeel free to explore the code, suggest improvements, and contribute to making the **Steam Game Recommender** even better!","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Femmanuelletocs%2Fsteam-game-recommender","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Femmanuelletocs%2Fsteam-game-recommender","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Femmanuelletocs%2Fsteam-game-recommender/lists"}