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Recommenders - Recommender Systems
- 348
Several utilities are provided in reco_utils to support common tasks such as loading datasets in the format expected by different algorithms, evaluating model outputs, and splitting train/test data. Implementations of several state-of-the-art algorithms are provided for self-study and customization in your own applications. Please see the setup guide for more details on setting up your machine locally, on Spark, or on Azure Databricks.
https://github.com/Microsoft/Recommenders| Tags | recommender-system machine-learning-algorithms ranking recommendation-algorithms |
| Implementation | Jupyter Notebook |
| License | MIT |
| Platform |
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