Recommender System Training Package

Hands-on Training for Recommender Systems developed for Machine Learning Essentials 2020.

In order to set up the necessary environment:

1. create an environment recsys_training with the help of conda,

   conda env create -f environment.yaml
   

2. activate the new environment with

   conda activate recsys_training
   

3. install recsys_training with:

   python setup.py install # or develop
   

Make sure you have docker and docker-compose installed.

1. Build the image with using the Dockerfile in docker

   docker build -t recsys-training:mle -f Dockerfile .
   

2. Start the container with docker-compose pointing to the yaml-file

   docker-compose up -f docker/docker-compose.yaml
   

The jupyter lab port 8888 will be mapped to the same port on your host machine, simply got to your preferred browser and enter via https://localhost:8888/

There are 9 notebooks within notebooks/ each starting with a number followed by _e_ for exercise. Within notebooks/solutions/you will find all notebooks with a solution proposal implemented. It is strongly advised to go through the notebooks in numerically ascending order.

We use MovieLens 100k as example dataset for the lessons. You can find the data in data/raw/.

This project has been set up using PyScaffold 3.2.3 and the dsproject extension 0.4. For details and usage information on PyScaffold see https://pyscaffold.org/.