GithubHelp home page GithubHelp logo

mukeshsinghmanral / react-flask-movie-recommendation-app Goto Github PK

View Code? Open in Web Editor NEW

This project forked from shohan2001/react-flask-movie-recommendation-app

0.0 0.0 0.0 3.51 MB

Movie Recommendation Website

Shell 0.09% JavaScript 54.96% Python 6.01% CSS 31.52% HTML 7.36% Procfile 0.05%

react-flask-movie-recommendation-app's Introduction

React.js + Flask Movie Recommendation System

Python Framework Frontend API

Overview ๐Ÿ“‹

  1. The web app is built using React.js for the front-end and python's flask for the back-end.
  2. It enable user to search and go through various details (like cast, genre, trailer, etc) 5000+ movies (all these details are fetched using an API by TMDB) .
  3. Based on the searched movie users are recommended movie which are fetched for the python-flask backend that uses local dataset and content-based filtering algorithm for recommendation.
  4. The web-app also allows user to get top movies filtered by genre (these are also fetched using an TMDB api) .
  5. The web app is responsive and can be used on mobile devies.

Maintenance Website shields.io


Installation ๐Ÿ“ฆ

  1. Clone or download this repository to your local machine.

  2. Install all the libraries mentioned in the [requirements.txt]

    $ pip install -r requirements.txt
  3. Then run the flask server by

    $ python app.py
  4. Go to the movie-recommender-app directory and install the node modules and build the project.

    $ cd movie-recommender-app
    $ npm install
  5. Go to the package.json file and change the proxy to your flask server local port which is most likely localhost:5000

  6. Then build the project by

    $ npm run build
  7. To the local flask server to start the project

    localhost :portNumber

  8. If this doesn't work use

    $ npm start

Architecture ๐Ÿ“„

image


Algorithm For Recommendation

The Recommendations are made by computing similarity scores for movies using consine simarity. For each movie tags are created by combining various details like genre of the movie, title, top cast, director and then they are converted to vectors using which similarity matrix is formed. Then for any searched movie the movies with the largest similarity score with it are sorted and then recommended.

Cosine Similarity

image


References

  1. TMDB's API : https://www.themoviedb.org/documentation/api
  2. Cosine Similarity : https://www.machinelearningplus.com/nlp/cosine-similarity/

react-flask-movie-recommendation-app's People

Contributors

shohan2001 avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.