Flick Movie Recommendation is a content-boosted recommendation system that provides users with movie recommendations based on their past interaction with the system.
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Table of Contents
The aim of this project is to build a web based application that will recommend movies to users that they might want to watch.
For this a content-boosted recommendation system is implemented that make use of ratings as well as comments to weight the recommendations.
Here's why:
- The application only needs a web browser to work and can work on low-end devices.
- The application does not violet any legal requirement the user’s data is kept safe within the system as well as it does not violet any content laws.
- The application is working with an average latency of less than 100ms.
Recommendation System folder have scripts for movie recommendation.
Sentiment Analysis folder have scripts and model params for sentiment analysis (under development).
- Python 3 >=3.4 How to download python
- pip
pip install --upgrade pip
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Clone the repo
git clone https://github.com/<your_username>/flick.git
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Go to Recommendation System folder and install dependencies
pip install -r requirements.txt
In the project directory, you can run:
python main.py
Server will start running at http://localhost:5001.
Any contributions you make are greatly appreciated.
- Fork the Project
- Create your Feature Branch (
git checkout -b some-new-feature
) - Commit your Changes (
git commit -m 'Add some feature'
) - In case of multiple commits squash them. Refer documentation
- Push to the Branch (
git push origin some-new-feature
) - Open a Pull Request
Distributed under the GNU General Public License v3.0. See LICENSE
for more information.
Lakshya Bansal - lakshyabansal
Project Link: https://github.com/lakshya-20/flick