As MLlib's recommendation model is based on matrix factorization, we can use the factor matrices computed by our model to compute predicted scores (or ratings) for a user. We will focus on the explicit rating case using MovieLens data; however, the approach is the same when using the implicit model.
The MatrixFactorizationModel class has a convenient predict method that will compute a predicted score for a given user and item combination as shown in the following code:
val predictedRating = model.predict(789, 123)
The output is as follows:
14/03/30 16:10:10 INFO SparkContext: Starting job: lookup at MatrixFactorizationModel.scala:4514/03/30 16:10:10 INFO DAGScheduler: Got ...