recsyschallenge2018

This repository contains code used to generate results submitted for the Spotify RecSys Challenge 2018 by Team2Step (Dennis Leszkowicz), placed #74 on the leaderboard for the main track.

Python 3.6 is recommended, as the final code has not been tested in Python 2.7

Requirements:

Make sure to install needed dependencies in requirements.txt by running pip install -r pip-requirements.txt

• implicit, used for fast Collaborative filtering on the implicit dataset
• annoy for fast approximate nearest neighbor search
• sciPy used for coo_matrix in implicit Alternating Least Squares
• numpy
• pandas

About Million Playlist Dataset (MPD)

The dataset consists of 1 million user generated playlists and comes in .json slices.

1) Loading the MPD

1. Run the following command:

python mpd_reader.py --input <FOLDERPATH to MPD source>

The script reads and trim the MPD json source files, retaining pid and track_uri saving it to a .tsv file in my_data/ directory.

2) Preprocessing the MPD and challenge-set

1. Run the following command:

python data_preprocessing.py --input <FOLDERPATH to challenge-set>

3) Generating recommendations for the challenge-set

1. Run the following command:

python recsys_recommender.py --input ../my_data/mpd_ch.tsv --output ../results/my_results.csv --model annoy_als --recommend

to use CPU for computing results make sure to set use_gpu : False as shown below

params = {'factors': 128, 'iterations': 15, 'dtype': numpy.float32, 'use_gpu': False, 'calculate_training_loss':True, 'use_cg':True, 'use_native':True}

saves a csv submission file to results/ directory

License

Usage of the Million Playlist Dataset is subject to these license terms