https://github.com/deezer/sigir2019-2stagesampling
Improving Collaborative Metric Learning for Recommendation by a 2-stage negative sampling strategy.
https://github.com/deezer/sigir2019-2stagesampling
Last synced: 4 months ago
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Improving Collaborative Metric Learning for Recommendation by a 2-stage negative sampling strategy.
- Host: GitHub
- URL: https://github.com/deezer/sigir2019-2stagesampling
- Owner: deezer
- Created: 2019-04-16T16:45:29.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2019-06-25T08:54:09.000Z (about 7 years ago)
- Last Synced: 2024-04-16T11:27:18.798Z (about 2 years ago)
- Language: Python
- Size: 21.5 KB
- Stars: 16
- Watchers: 3
- Forks: 5
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Improving Collaborative Metric Learning with Efficient Negative Sampling
This repository contains implementation for the following paper:
> V-A. Tran, R. Hennequin, J. Royo-Letelier and M. Moussallam. Improving Collaborative Metric Learning with Efficient Negative Sampling. In: *Proceedings of SIGIR 2019*, July 2019.
## Environment
- python 3.6
- tensorflow 1.12
- numpy 1.15.4
- scipy 1.1.0
- sklearn 0.20.2
- pandas 0.24.2
## Example dataset
- Download the Echonest dataset (TRIPLETS FOR 1M USERS) from [here](http://millionsongdataset.com/tasteprofile/).
- Put the unzipped files into directory data/echonest
## Run example script
Run experiments/exp_echonest.sh for an example:
- UNIFORM ndcg/map: [0.067834 0.03042152], MMR: 358.485
- POPULAR ndcg/map: [0.08187394 0.04360328], MMR: 30.783
- 2-STAGE ndcg/map: [0.09179041 0.04784787], MMR: 146.327