https://github.com/opringle/gluonrank
Ranking made easy
https://github.com/opringle/gluonrank
collaborative-filtering gluon mxnet ranking
Last synced: 5 months ago
JSON representation
Ranking made easy
- Host: GitHub
- URL: https://github.com/opringle/gluonrank
- Owner: opringle
- License: gpl-3.0
- Created: 2018-11-26T02:37:37.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2019-01-09T18:28:18.000Z (over 6 years ago)
- Last Synced: 2024-05-22T04:21:23.875Z (11 months ago)
- Topics: collaborative-filtering, gluon, mxnet, ranking
- Language: Python
- Size: 62.5 KB
- Stars: 36
- Watchers: 5
- Forks: 4
- Open Issues: 1
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Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- Awesome-MXNet - gluon-rank
README
### GluonRank: Your Choice of Deep Learning for Ranking
GluonRank is a toolkit that enables easy implementation of collaborative filtering models using neural networks, to help your prototyping of state of the art ranking systems.
## Installation
### Pip
Make sure you are using Python 3.6. You can install `MXNet` and `GluonRank` using pip:
```bash
pip install --index-url https://test.pypi.org/simple/ gluonrank
```
### Uploading to pypi for testing
Build distribution
`python setup.py sdist bdist_wheel`bash
Upload to pypi test index
`twine upload --repository-url https://test.pypi.org/legacy/ dist/*`bash
## Docs
Coming soon... (it might be a while actually...)
## ToDo
- [ ] Categorical features
- [ ] Get running with multiple categorical features, maintain performance when reducing to a single one
- [ ] Gracefully handle missing continuous embedding or categorical variables & user/item biases
- [ ] Do not require user to index their embedding values for a single matrix
- [ ] Continuous features
- [ ] Get running with 1 continous feature, maintain performance when excluded
- [ ] Get running with several continuous features
- [ ] Increase the efficiency of the evaluation function
- [ ] Speed up negative sampling... Negative sampling without collisions results in 5X training time.
(answer)[https://stackoverflow.com/questions/53576915/sample-n-zeros-from-a-sparse-coo-matrix/53577344#53577344]
- [x] Match spotlight performance with implicit interaction model on movielense data
- [x] Build ranking function as network method
- [ ] Create python package
-
- [ ] Create hosted docs
## Features
- [ ] Allow for sampling more than one negative per interaction
- [ ] Allow for feedback that can be in the form of 0, 1 or -1. (eg swiping data)
## Ideas