Machine Learning with Spark - Second Edition

Mean Squared Error

The MSE is a direct measure of the reconstruction error of the user-item rating matrix. It is also the objective function being minimized in certain models, specifically many matrix-factorization techniques, including ALS. As such, it is commonly used in explicit ratings settings.

It is defined as the sum of the squared errors divided by the number of observations. The squared error, in turn, is the square of the difference between the predicted rating for a given user-item pair and the actual rating.

We will use our user 789 as an example. Let's take the first rating for this user from the moviesForUser set of Ratings that we previously computed:

val actualRating = moviesForUser.take(1)(0)

Here is the output:

actualRating: ...