https://github.com/sainathadapa/spotify-sequential-skip-prediction
7th place solution to the WSDM Cup 2019 - Spotify - Sequential Skip Prediction Challenge
https://github.com/sainathadapa/spotify-sequential-skip-prediction
deep-learning music-recommendation rnn wsdm
Last synced: about 1 year ago
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7th place solution to the WSDM Cup 2019 - Spotify - Sequential Skip Prediction Challenge
- Host: GitHub
- URL: https://github.com/sainathadapa/spotify-sequential-skip-prediction
- Owner: sainathadapa
- License: apache-2.0
- Created: 2019-01-09T16:02:26.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2019-06-01T13:46:45.000Z (about 7 years ago)
- Last Synced: 2025-06-19T23:12:44.565Z (about 1 year ago)
- Topics: deep-learning, music-recommendation, rnn, wsdm
- Language: Jupyter Notebook
- Homepage: https://arxiv.org/abs/1904.10273
- Size: 668 KB
- Stars: 27
- Watchers: 2
- Forks: 5
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# WSDM Cup 2019 - Spotify - Sequential Skip Prediction Challenge - 7th place solution
You can find a report about my solution [here](workshop-paper-source/paper.pdf)
To know more about the challenge, refer the following links:
- https://www.crowdai.org/challenges/spotify-sequential-skip-prediction-challenge
- http://www.wsdm-conference.org/2019/wsdm-cup-2019.php
This repository's contents are shared under the Apache License 2.0.
To reproduce, follow the steps in order:
1. Download the dataset from the CrowdAI website.
2. (Optional) Create sample datasets using the [04-create-samples.ipynb](04-create-samples.ipynb) notebook.
3. Train the models using the [05-train-each-len.py](05-train-each-len.py) script.
4. (Optional) Evaluate the models on the validation set using [06-predict-each-len-val-set.py](06-predict-each-len-val-set.py).
5. Process the test data using the [07-process-test-file.py](07-process-test-file.py).
6. Finally, the submission file can be created by using the [09-create-submission-one-test-file.py](09-create-submission-one-test-file.py) script.
## Citing
```
@article{adapa2019sequential,
title={Sequential modeling of Sessions using Recurrent Neural Networks for Skip Prediction},
author={Adapa, Sainath},
journal={arXiv preprint arXiv:1904.10273},
year={2019}
}
```