krsnvijay / ml-spotify-recommendations

COMP 6321 Machine Learning Project - Group 18

Recommender system for spotify 1M playlist dataset using k-NN and NeuMF models

Main Dependencies

• Pytorch
• Pandas
• Scikit learn
• Numpy
• Spotipy

Folder Structure

./code/environment.yaml <-- conda environment for our project
./code/preprocessing.ipynb <-- notebook to preprocess our data
./code/recommender_models.ipynb <-- primary notebook to run the k-NN and NeuMF models
./code/raw_data/ <-- subset of spotify's original 5gb dataset
./code/dataframes/ <-- preprocessed dataframes containing playlists, tracks, genres
./code/model/ <-- trained neuMF model will save to this folder when run

Setup

• create conda environment from environment.yml using

conda env create -f environment.yml

• activate your conda environment using

conda activate recommender

• If using a jupyter notebook, use this command to create this pykernel, later change your kernel to this on jupyter.

python -m ipykernel install --user --name recommender --display-name "Recommender"

Instructions

• recommender_models.ipynb is the main notebook to run
• it uses preprocess dataframes that are in the dataframes/ folder so there is no need to run preprocessing.ipynb again
• GPU is required to train the model faster, but our code is GPU agnostic will also work with a cpu
• Training takes ~10 min with GPU

Dataset

The spotify playlists dataset was downloaded from https://www.aicrowd.com/challenges/spotify-million-playlist-dataset-challenge

Acknowledgements

Some of the resources we've used to learn how to create a recommender model

1. https://github.com/microsoft/recommenders - Has the best practices for recommender systems
2. https://github.com/guoyang9/NCF - as a reference to build our NeuMF model