Codes-for-WSDM-CUP-Music-Rec-1st-place-solution

This is the corresponding codes for WSDM CUP 2018 Music Recommendation Challenge's 1st place solution.

Please create following folders before testing:

• input/training/source_data/

• input/training/temporal_data/

• input/validation/source_data/

• input/validation/temporal_data/

• temp_nn/

• submission/

Put the data in the folder "source_data", then run script/run.sh, features will be extracted. For validation, you need to prepare data by hand, and create a "test_label.csv" file with "target" field.

The hyper-parameters is recorded in lgb_record.csv and nn_record.csv, you can try it directly. If everything is right, you should be able to get 0.744+ with LightGBM, and 0.742+ with 30-ensemble of NNs. 0.6 * LightGBM + 0.4 * NN should be able to get you ~0.749.

The code is tested on a small part of the data under python 2.7, if you find any bug, please contract me under the topic on Kaggle.

The versions of dependencies:

• pandas: 0.20.1

• sklearn: 0.18.1

• keras: 2.0.4

• lightgbm: 0.1

• numpy: 1.12.1

• scipy: 0.19.0

• Tensorflow 1.0.1