Building Recommender Systems

This is a utility package for the course SMEMI315: Building Recommender systems taught at the Université Côte d'Azure in autumns of 2021 and 2022. See the course description for more info about its content.

How to Install

In a Docker container

First, install Docker on your system. Then:

docker build -t recommender-systems-course https://github.com/inpefess/recommender-systems-course.git
docker run -it --rm -p 8888:8888 recommender-systems-course jupyter-lab --ip=0.0.0.0 --port=8888

You can also try it on MyBinder (it's a free system, so resources are not always available).

On Linux

This includes Google Colab, Kaggle, and VertexAI Workbench.

pip install git+https://github.com/inpefess/recommender-systems-course.git

Depending on your environment (this is a known issue on VertexAI Workbench, for example), you should add other parameters to this command.

PATH=/home/jupyter/.local/bin:$PATH; pip install --user git+https://github.com/inpefess/recommender-systems-course.git

On Windows

There are two options:

1. Use Windows Subsystem for Linux (WSL)

   On WSL, proceed as on any Linux.

2. Use Anaconda

   # get the source
   git clone git+https://github.com/inpefess/recommender-systems-course.git
   cd recommender-systems-course
   # use a provided environment configuration
   conda env create -n recommender-systems-course -f environment.yml python=3.8
   conda activate recommender-systems-course
   # test that all installed correctly
   pytest
   # add ``rs_course`` package
   pip install .
   # start working
   jupyter lab

On macOS

Should be in principle installable in a similar way as on Linux, but not tested.

How to Use

This package is supposed to be used together with rs_datasets:

from rs_datasets import MovieLens

ratings = MovieLens("small").ratings

The package contains pre-packed examples of different recommenders. For example, this function computes hit-rate@1 of a PureSVD trained with default parameteres from scikit-learn on randomly selected 80% of ratings:

from rs_course.cf_svd import pure_svd_recommender

pure_svd_recommender(
      ratings=ratings,
      split_test_users_into=1,
      model_config={},
      top_k=1,
      train_percentage=0.8
)

More Detailed Documentation

More detailed documentation is available here.

Similar packages

This package is not supposed to be used as a recommender systems library. It's only purpose is to help a complete beginner to get the taste of the recommenders' world. For a proper library, try something from this list.

How to Cite

If you want to cite this package in your research paper, please use the following DOI.