Technical requirements
In this chapter, we will use the following Python libraries and versions to create content- and rating-based recommendation engines, as well as hybrid and online recommenders:
azureml-core 1.34.0
azureml-sdk 1.34.0
numpy 1.19.5
scipy 1.7.1
pandas 1.3.2
scikit-learn 0.24.2
lightgbm 3.2.1
pyspark 3.2.0
azure-cognitiveservices-personalizer 0.1.0
Similar to previous chapters, you can run this code using either a local Python interpreter or a notebook environment hosted in Azure Machine Learning.
For the Matchbox recommender example, you need to use Azure Machine Learning designer in your Azure Machine Learning workspace. For Azure Personalizer, you need to set up an Azure Personalizer resource in the Azure portal.
All code examples in this chapter can be found in the GitHub repository for this book: https://github.com/PacktPublishing/Mastering-Azure-Machine-Learning-Second-Edition/tree/main/chapter13.
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