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Release notes Recommenders 1.1.1 #1793

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Jul 19, 2022
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Release notes Recommenders 1.1.1 #1793

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8 changes: 8 additions & 0 deletions NEWS.md
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# What's New

## Update July, 2022

We have a new release [Recommenders 1.1.1](https://github.com/microsoft/recommenders/releases/tag/1.1.1)!

We have introduced a new way of testing our repository using [AzureML](https://azure.microsoft.com/en-us/services/machine-learning/). With AzureML we are able to distribute our tests to different machines and run them in parallel. This allows us to test our repository on a wider range of machines and provides us with a much faster test cycle. Our total computation time went from around 9h to 35min, and we were able to reduce the costs by half. See more details [here](tests/README.md).

We also made other improvements like faster evaluation metrics and improving SAR algorithm.

## Update April 1, 2022

We have a new release [Recommenders 1.1.0](https://github.com/microsoft/recommenders/releases/tag/1.1.0)!
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15 changes: 7 additions & 8 deletions README.md
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[![Documentation Status](https://readthedocs.org/projects/microsoft-recommenders/badge/?version=latest)](https://microsoft-recommenders.readthedocs.io/en/latest/?badge=latest)

## What's New (April 1, 2022)

We have a new release [Recommenders 1.1.0](https://github.com/microsoft/recommenders/releases/tag/1.1.0)!
We have introduced the SASRec and SSEPT algorithms that are based on transformers.
In addition, we now have enabled Python 3.8 and 3.9.
We have also made improvements on the SARPlus algorithm, including support for Azure Synapse and Spark 3.2.
There are also bug fixes and improvements on NCF, RBM, LightGBM, LightFM, Scikit-Surprise, the stratified splitter, dockerfile
and upgrade to Scikit-Learn 1.0.2.
## What's New (July, 2022)

We have a new release [Recommenders 1.1.1](https://github.com/microsoft/recommenders/releases/tag/1.1.1)!

We have introduced a new way of testing our repository using [AzureML](https://azure.microsoft.com/en-us/services/machine-learning/). With AzureML we are able to distribute our tests to different machines and run them in parallel. This allows us to test our repository on a wider range of machines and provides us with a much faster test cycle. Our total computation time went from around 9h to 35min, and we were able to reduce the costs by half. See more details [here](tests/README.md).

We also made other improvements like faster evaluation metrics and improving SAR algorithm.

Starting with release 0.6.0, Recommenders has been available on PyPI and can be installed using pip!

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