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datamining_recommender_system

https://github.com/jean3p/datamining_recommender_system

Science Score: 26.0%

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    CITATION.cff file
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    Found .zenodo.json file
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    Scientific vocabulary similarity
    Low similarity (11.7%) to scientific vocabulary
Last synced: 9 months ago · JSON representation

Repository

Basic Info
  • Host: GitHub
  • Owner: jean3P
  • License: mit
  • Language: Python
  • Default Branch: main
  • Size: 110 MB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created over 2 years ago · Last pushed over 2 years ago
Metadata Files
Readme License Citation

README.md

DataMining Recommender System for Twitch (TWITCHCOMM)

Overview

The DataMining Recommender System is designed to enhance the user experience on Twitch by providing personalized streamer recommendations. This system leverages data mining techniques to predict communities within the Twitch social network and recommend streamers based on these community affiliations.

Repository Structure

  • backend_rs: Contains the Django backend server, responsible for data processing, API management, and serving recommendation data.
    • twitch_app: Core application handling Twitch data processing and community prediction.
  • frontend_rs: React-based frontend integrated with Electron, offering a user-friendly interface for displaying recommendations.
  • training_rs: Includes scripts and resources for training the machine learning models used in community prediction.
    • community_prediction: Directory dedicated to developing models for predicting Twitch communities.
  • LICENSE: Project's license file.
  • README.md: This file, providing an overview and instructions for the project.

Setup and Installation

  1. Backend Setup:

    • Navigate to the backend_rs directory.
    • Install required dependencies: pip install -r requirements.txt
    • Run the Django server: python manage.py runserver

  2. Frontend Setup:

    • Go to the frontend_rs directory.
    • Install necessary packages: npm install
    • Start the React application: npm start

  3. Training the Models:

    • In the training_rs directory, run the training scripts to generate the community prediction models.

Usage

  • After starting the Django server and the React application, access the frontend via a web browser.
  • Enter your Twitch username to receive personalized streamer recommendations.

Contributing

Contributions to the DataMining Recommender System are welcome. Please read CONTRIBUTING.md for details on our code of conduct and the process for submitting pull requests.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

  • Twitch API
  • Community contributors and users of the system

Owner

  • Name: Jean 2P Principe
  • Login: jean3P
  • Kind: user

As a highly motivated and adaptable professional with a three years background in Java Software Engineer, I bring a strong track record of object oriented progr

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