The recommender system behind GroupStreamer.
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api
data
itunes
lfm-ratings
libs
lsa
scripts
tagfetch
www
.gitignore
README.md
bootstrap.sh
config.yaml.sample
python-reqs.txt
schema.sql

README.md

Unison

Unison is the recommendation system behind GroupStreamer, an Android application that recommends music for groups. You can also checkout the source code of the mobile application if you're interested.

Getting started

You need to define an environment variable, UNISON_ROOT, that contains the path to Unison's root folder. Then, run bootstrap.sh which will initialize some things.

# Create the one environment variable we need, and run bootstrap.sh.
export UNISON_ROOT=`pwd`
./bootstrap.sh

Unison is mainly written in Python, and uses various libraries. The simplest thing to do is to create a new virtual environment dedicated to Unison.

# Setup a python virtual environment and install the dependencies.
virtualenv --distribute venv
. venv/bin/activate
pip install -r python-reqs.txt

# Unison is currently bundled with two libraries.
pip install libs/libunison libs/liblfm

Some scripts also require numpy, scipy and matplotlib. If you want to be able to run everything, you should also install those. Be aware that you'll need a Fortran compiler.

# Don't have a fortran compiler? On Mac and with Homebrew, type:
brew install gfortran

# Install the Python numerical analysis & computation trifecta.
pip install numpy
pip install scipy
pip install matplotlib

# You might also have to install scikit-learn to be able to run everything.
pip install scikit-learn

Unison uses many other tools and software. Hopefully most of it is documented in the relevant places (checkout the README files in the various subfolders).

One last (important) thing: there's a central configuration file that is expected to live at $UNISON_ROOT/config.yaml. Check out the sample configuration file to see what's expected to be in there.

Project structure

The project comprises several components, usually organized as subfolders of the root directory. Here's a brief description of the main parts.

  • data: contains the raw data from the Million Song Dataset, used to build the latent space.
  • lsa: stands for Latent Semantic Analysis. Everything related to building the tag-based recommendation system.
  • tagfetch: the background service that fetches information about tracks from Last.fm (tags, cover art, ...).
  • api: the REST API used by the mobile application to communicate with the recommendation system.
  • www: the main website.
  • scripts: several utilities that are used to manipulate or visualize the data, or perform some maintenance operation.
  • lfm-ratings: experiments done on user rating data obtained from Last.fm. The purpose was to test the effectiveness of the tag-based latent space representation of tracks.
  • libs: contains the Python libraries bundled with Unison.