https://github.com/ashudevcodes/steam-game-recommendation-system

Creating a Steam game recommender using content-based suggestions, analyzing tags, descriptions, genres. Limited data from 2021-2023, focused on recent releases.
https://github.com/ashudevcodes/steam-game-recommendation-system

recommender-system steam steam-games streamlit-webapp

Last synced: 5 days ago
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Creating a Steam game recommender using content-based suggestions, analyzing tags, descriptions, genres. Limited data from 2021-2023, focused on recent releases.

• Host: GitHub
• URL: https://github.com/ashudevcodes/steam-game-recommendation-system
• Owner: ashudevcodes
• License: mit
• Created: 2023-08-11T05:41:35.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-07-28T15:41:28.000Z (6 months ago)
• Last Synced: 2024-11-27T23:13:44.465Z (2 months ago)
• Topics: recommender-system, steam, steam-games, streamlit-webapp
• Language: Jupyter Notebook
• Homepage: https://huggingface.co/spaces/ashuNicol/Steam-game-Recommendation-System
• Size: 49.3 MB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Steam Game Recommendation System

https://github.com/user-attachments/assets/ae766122-af48-4b77-937c-17437cfb41a9

This project aims to build a Steam game recommendation system using content-based suggestions. It analyzes game tags, descriptions, and genres to provide personalized recommendations to users. The system focuses on games released between 2021 and 2023.

## Features

- Content-based recommendation: Analyzes game tags, descriptions, and genres to suggest similar games.

- Limited data: Uses data from 2021 to 2023 for recent game releases.

  

## Installation

1. Clone the repository: `git clone https://github.com/Ashishprasa/Steam-Game-Recommendation-System.git`

2. Install dependencies: `pip install -r requirements.txt`

3. After completing the installation, navigate to the cloned repository in the terminal and execute `streamlit run app.py`

4. If you try the Web app without cloning it, I will add the HuggingFace Space under the 'About' section.

## Usage

1. Ensure you have the required data in the specified format (tags, descriptions, genres).

2. Run the recommendation script: `streamlit run app.py`

3. Input user preferences.

4. Receive personalized game recommendations.

## Data

The project utilizes game data from 2021 to 2023, including titles like "Stray" and "GTA Trilogy".

## Contributions

Contributions are welcome! If you'd like to add features, improve the recommendation algorithm, or fix issues, feel free to submit a pull request.

## License

This project is licensed under the [MIT License](LICENSE).