Recommender

Post recommendation service for Fennec.

Quickstart

# Install pyenv (https://github.com/pyenv/pyenv)
git clone https://github.com/pyenv/pyenv.git ~/.pyenv
echo 'export PYENV_ROOT="$HOME/.pyenv"' >> ~/.bash_profile
echo 'export PATH="$PYENV_ROOT/bin:$PATH"' >> ~/.bash_profile
echo -e 'if command -v pyenv 1>/dev/null 2>&1; then\n  eval "$(pyenv init -)"\nfi' >> ~/.bash_profile

# Install python dependencies
sudo apt install build-essential libsqlite3-dev sqlite3 bzip2 libbz2-dev libffi-dev

# Install python
pyenv install 3.7.5

# Install dependency manager (https://poetry.eustace.io)
python -m pip install --user poetry

# Install dependencies
python -m poetry install

# Edit config file
cp env.sample .env
vim .env

# Train model
python -m poetry run python bin/train

# Run webserver (development)
python -m poetry run flask run
python -m poetry run gunicorn wsgi

# Get recommendations for user #1
curl http://localhost:5000/recommend/1

# Get recommendations for post #1
curl http://localhost:5000/similar/1

# Get model info (user/post counts, training time, etc.) as JSON
curl http://localhost:5000/info

# Get Prometheus metrics
curl http://localhost:5000/metrics

System requirements

Training on the full dataset of ~80 million favorites takes ~17 minutes (on an E5-1650v4) and requires ~4GB of RAM. The trained model requires ~2GB of RAM.