cosernn

A reference implemententation of the CoSeRNN model for contextual music recommendation, presented in the following paper:

Casper Hansen, Christian Hansen, Lucas Maystre, Rishabh Mehrotra, Brian Brost, Federico Tomasi, Mounia Lalmas. Contextual and Sequential User Embeddings for Large-Scale Music Recommendation, RecSys 2020.

Getting Started

Our implementation requires Python 3.7 and TensorFlow 1.x. To run the code, you will need a CUDA-enabled GPU.

To get started, simply follow these steps:

• Clone the repo locally with: git clone https://github.com/spotify-research/cosernn.git
• Move to the repository with: cd cosernn
• install the dependencies: pip install -r requirements.txt
• install the package: pip install -e lib/

Generate data using

python scripts/generate_data.py

Train the CoSeRNN model using

python scripts/train.py path/to/records

Support

Create a new issue

Contributing

We feel that a welcoming community is important and we ask that you follow Spotify's Open Source Code of Conduct in all interactions with the community.

Authors

• Casper Hansen
• Lucas Maystre

A full list of contributors can be found on GitHub.

Follow @SpotifyResearch on Twitter for updates.

License

Copyright 2020 Spotify, Inc.

Licensed under the Apache License, Version 2.0: https://www.apache.org/licenses/LICENSE-2.0

Security Issues?

Please report sensitive security issues via Spotify's bug-bounty program (https://hackerone.com/spotify) rather than GitHub.