Recommenders 0.1.1

New Algorithms or improvements

• Improved SAR single node for top k recommendations. User can decide if the recommended top k items to be sorted or not.

New utilities or improvements

• Added data related utility functions like movielens data download in Python and PySpark.
• Added new data split method (timestamp based split) added.

New Notebooks or improvements

• Added an O16N notebook for Spark ALS movie recommender on Azure production services such as Databricks, Cosmos DB, and Kubernetes Services.
• Added SAR deep dive notebook with single-node implementation demonstrated.
• Added Surprise SVD deep dive notebook.
• Added Surprise SVD integration test.
• Added Surprise SVD ranking metrics evaluation.
• Made quick-start notebooks consistent in terms of running settings, i.e., experiment protocols (e.g., data split, evaluation metrics, etc.) and algorithm parameters (e.g., hyper parameters, remove seen items, etc.).
• Added a comparison notebook for easy benchmarking different algorithms.

Other features

• Updated SETUP with Azure Databricks.
• Added SETUP troubleshooting for Azure DSVM and Databricks.
• Updated READMEs under each notebook directory to provide comprehensive guidelines.
• Added smoke/integration tests on large movielens dataset (10mil and 20mil).
• Updated the Spark settings of CI/CD machine to eliminate unexpected build failures such as "no space left issue".