Publication of the code we used in the WSDM'18 Cup Music Recommendation Challenge.

• Place data files in the data/ and data_common/ directory
• Run the python scripts in the following order
  • split.py (Optional, creates a local sample)
  • prep_songs.py (Some extra data preparation and combination, requires the cleaning of songs.csv as songs_clean.csv)
  • prep.py (Some dataset preparations)
  • als.py (Creates the latent features)
  • cluster.py (Clusters the latent features for users, tracks, artists, and combinations of those)
  • create_featureset.py (Combines all data in one dataset to work on)
  • lgbm_test.py (Tests a single LGBM model)
  • lgbm_cv.py (Creates the n-fold trained ensemble)
• The folder to do the work in is defined as FOLDER in each script along with other important parameters
• The file helper/feature_list.py defines a list of features that is finally used for model training