Fullstack RecSys Project

This repository is a toy project integrating machine learning with database, front-end and back-end. We aim to build a small web service to provide top-K movie recommendation from user profile. To do this, we implement back-end and database server with Flask, front-end with React and recommedation API with PyTorch and Numpy.

Setups

Requirements

This project requires packages such as Flask, PyTorch and numpy. Please check detailed list in requirements.txt. On your own environment, install required packages by pip install -r requirements.txt

Back-end

First, we build a minimal back-end server with Flask. Back-end server 1) initializes database so that API server can load data and train recommender models, 2) handles requests from front-end by communicating with API server and database.

Here, we initialize database with ML-100k, which is small but popular dataset with 100k from 1,000 users and 1,700 items. We provide pre-built database (app.db), however, if you want to build your own database, please follow the code below.

1. Initialize DB model by following scripts.

flask db init && 
flask db migrate &&
flask db upgrade

2. Initialize ML-100k into database.

cd backend && python initialize_ml100k_db.py

Front-end

Front-end is built with react and semantic-ui-react. You need npm to build and run react.

To build front-end, run npm install.

Recommendation API server

In this project, we aim to build recommender taht can provide recommendation to new users with their profile (Unseen in training but not cold-start). API server 1) trains a recommender model offline with database and save, 2) responds to the recommendation request from back-end server.

For now, we provide simple non-neural similarity and nearest neighbor models, EASE and ItemKNN.

• EASE: Harald Steck, Embarrassingly Shallow Autoencoders for Sparse Data. WWW 2019. Link
• ItemKNN: Jun Wang et al., Unifying user-based and item-based collaborative filtering approaches by similarity fusion. SIGIR 2006. Link

To train and save recommender offline, run

cd ./api && 
python fit_offline.py --model MODEL_NAME --save_dir PATH_TO_SAVE_MODEL

• model: name of a model to train (currently, EASE & ItemKNN are available.)
• save_dir: path to save model checkpoint

Run

To make the all components work together, we have to run API, back-end, front-end servers.

API Server

To run API server,

cd ./api && python api.py

By default, API server is run on 0.0.0.0:8000.

Back-end

To run back-end server,

cd ./backend && flask run

By default, back-end server is run on 127.0.0.1:5000.

Front-end

To run front-end,

cd ./react-front && npm start

You can specify host and port in react-front/.env. By default, front-end server is run on 127.0.0.1:5052.

Edit name and personal links (github, blog) in react-front/src/App.js:155.

Bind back-end & front-end

You can run front-end and back-end script together using concurrently, which helps you run scripts parallel.

You can install concurrently by npm install -g concurrently. In react-front/package.json, add binding script (e.g. "start-all": "concurrently \"react-scripts start\" \"cd ../backend && flask run\"").

Now, npm run-script start-all runs both back-end and front-end.

TODO

• Show movie metadata on click.
• Add SOTA neural and non-neural recommenders.
• Fancier front-end (Header, Footer, etc.)

Reference

1. How To Create a React + Flask Project
2. Image Recommendations with PyTorch + Flask + PostgreSQL + Heroku deployment
3. Build a fully production ready machine learning app with Python Django, React, and Docker
4. tfrs-movierec-serving