Skip to content

Latest commit

 

History

History
55 lines (38 loc) · 2.49 KB

README.md

File metadata and controls

55 lines (38 loc) · 2.49 KB
Raw

Movie Recommendation System

Movie recommendation system is a demo application made for purpose of demonstrating the use cases of Structured Query Engine. User can search for a movie using various filters provided and select a movie from search results to get recommended similar results.

Installation

  • Recommended versions: Python >= 3.5.2 and Node >= 6.0.0
  • Copy sample.config.json to config.json
  • Use pip install -r requirements.txt to install python dependencies
  • cd app/frontend && npm install

Running

  • Edit config.json's field according to conveinence. query_engine_url is to be set to url of structured query engine
  • In the root folder, to get the movie_metadata.csv do wget -O movie_metadata.csv https://goo.gl/YRj8dV
  • Run the structured query engine
  • Feed the structured query engine using python -m scripts.test_query_engine

Running: Production build

  • From root folder cd app/frontend and do npm run build. This will generate a production build ready to be used.
  • Run the backend using python start.py
  • Go to config_server_url:config_server_port to see the app in action.
  • You will always need to run npm run build each time to change something in js files or config.json

Running: Development build

  • Run the backend using python start.py
  • From root folder cd app/frontend and do npm start. This will open a page on localhost:3000 which will hot reloaded whenever a change is made to frontend files.

Note: You can use NVM to install versions of node.

Features

  • Built using ReactJS, React Router, React Bootstrap and Tornado Web Framework
  • Single page application
  • Use axios promise based AJAX requests for backend communication.

Architecture

Architecture

Search controller handles multi filter search requests from frontend and Recommendation controller handles gathering recommendations for a particular movie from Structured Query Engine.

Screenshot

Screenshot

Authors

Credits

We will like to thank our Search Engine Architecture course at NYU's professor Matt Doherty.

License

Apache License V2