The Matchbox recommender

Microsoft has developed a large-scale recommender system based on a probabilistic model (Bayesian) called Matchbox. This model can learn about a user's preferences through observations made on how they rate items, such as movies, content, or other products. Based on those observations, it recommends new items to the users when requested.

Matchbox uses the available data for each user in the most efficient way possible. The learning algorithm it uses is designed specifically for big data. However, its main feature is that Matchbox takes advantage of metadata available for both users and items. This means that the things it learns about one user or item can be transferred across to other users or items.

You can find more information about the Matchbox model at the Microsoft Research project link.

Types of recommendations

The Matchbox recommender supports the building of four kinds of recommenders, which will include most of the scenarios. Let's take a look at the following...