Machine Learning with ML.NET - Recommendation Systems

Machine Learning with ML.NET - Recommendation Systems

In this article, we explore the various recommendation systems and implement Matrix Factorization with ML.NET.

From Netflix, Google, and Amazon, to smaller webshops, recommendation systems  are everywhere. In fact, this type of system represents probably one of the most successful business applications  of Machine Learning. Their ability to predict what users would like to read, watch and buy proved to be good not only for the business but for the users as well. For users, they provide a way to explore product space and for businesses they provide an increase in user engagement and more knowledge about the customers. Also, these systems are widespread  and existing in almost every big cloud platform. When we think of YouTube video recommendations, they are there. Netflix menus with suggested series, they are turning the wheels behind the scene. Gmap suggested routes? You can bet. These systems became one of the building blocks of our industry and it would be bad not to know anything about them. In this article, we get familiar with these systems and see how we can build one using ML.NET .

The topics covered in this article are:

  1. Dataset and Prerequisites
  2. Types of Recommendation Systems
  3. Collaborative Filtering Intuition
  4. Matrix Factorization Intuition
  5. Implementation with ML.NET

artificial intelligence data science datascience deep learning dotnet ensemble learning matrix factorization recommendation systems software craft

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