Applying adversarial autoencoding recommender to Spotify million playlist dataset
Challenge: RecSys Challenge 2018
Track: Main track
Team name: Unconscious Bias
Steps to train a model and apply it to a test set
After cloning the repository it takes very few action to apply our approach. Please make sure to run the code on a machine with GPUs and CUDA support. For the following command line instructions, the current working directory is assumed to be the present git repository.
Step 1: Setup virtual environment and install all dependencies
This will create a virtual environment in folder
venv and install all the necessary requirements.
Step 2: Activate the virtual environment
Step 3: Kick-off the experiments (can take a while)
CUDA support is required.
python3 make_submission.py --data-path PATH/TO/MillionPlaylist/data --test-path PATH/TO/MillionPlaylist/test_set.json
Replace the argument for
--data-path with the ./data directory of the Spotify Million Playlist Dataset and
replace the argument for
--test-path with the path to the json file holding test set.
Per default the output will be written to
submission.csv, if desired it can be changed by providing