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Jun 26, 2017


IMPORTANT: The current state of this sample application is BETA, consider it version a 0.1, therefore, many areas could be improved and change significantly. This is purely built with the purpose of showing off a concept at this point.


Recommendation Engine using Azure ML Studio to recommend related movies. The app relies on using the popular MovieLens 20M dataset.

The Azure Machine Learning model was built using the TrainBox Match Recommender which is a hybrid recommender. It uses both user-content and colloborative filtering to provide recommendations out-of-the-box

You can find more details about the app and how the Azure Machine Learning recommendation model is built through this blog

How to get this running!

  • Clone the repo and open the movierecommender.sln and build in Visual Studio 2017. Currently the app is only tested for Windows.
  • Start with this MovieLens Movie Recommendation model, this is based on the 1M dataset, you will need to replace the datasets to use the 20M dataset for better results.
  • After publishing your webservice, change the 'apikey' and 'uri' in the appsettings.json file with your webservice keys instead.

Feedback on this app

As mentioned, we'd appreciate to your feedback, improvements and ideas. You can create new issues at the issues section, do pull requests and/or send emails to


Recommendation Engine using Azure ML Studio to recommend related movies.



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