Releases: recommenders-team/recommenders
Releases · recommenders-team/recommenders
Recommenders 0.2.0
New Algorithms or improvements
- Vowpal Wabbit (VW) #452
- xDeepFM #453
- DKN #453
- NCF #392
- RBM #390
- FastAI Embedding dot Bias #411
- Optimization of SAR
New utilities or improvements
- Improved the performance of python splitters #517
- Added GPU utilities
- Added utilities for hyperparameter tuning
New Notebooks or improvements
- Improved o16n notebook with ALS, Movielens and Databricks #475
- Added a deep dive notebook on VW #452
- Improved notebook for hyperparameter tuning on Spark #444
- New notebook on FastAI Embedding dot Bias algo #411
- New notebook of deep dive on NCF #392
- New quick start notebook of RBM #390
- New deep dive notebook of RBM #390
- New quickstart notebook of xDeepFM with synthetic data
- New quickstart notebook of DKN with synthetic data
- New notebook on data transformation #384
Other features
- Fixed bugs in utilities, tests and notebooks
- Added an installation script for Databricks #457
- Changed installer from a bash to a python script #512
- Added a parameter to control pyspark version in the installer #461
- Optimized tests to be quicker #486
- New unit, smoke and integration tests for the new algos
- Added GPU test pipeline #408
- Improved Github metrics tracker #400
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".
Recommenders 0.1.0
New Algorithms or improvements
Development of SAR algorithm on three implementations:
New utilities or improvements
- Dataset splitters in Python and PySpark.
- Rating and ranking metrics in Python and PySpark.
New Notebooks or improvements
- ALS quickstart with Movielens
- SAR single node quickstart with Movielens
- SAR PySpark quickstart with Movielens
- SAR+ quickstart with Movielens
- Data splitter
- ALS deep dive
- SAR deep dive
- Evaluation
Other features
- Benchmark of the current algorithms.
- Unit, smoke and integration tests for Python and PySpark environments.