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gluonrank
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Ranking made easy
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### 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
