microsoft/recommenders

This repository provides examples and best practices for building recommendation systems, provided as Jupyter notebooks. 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 training/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. NOTE - The Alternating Least Squares (ALS) notebooks require a PySpark environment to run.