Database Reference
In-Depth Information
User recommendations
In this case, we would like to generate recommended items for a given user. This usually
takes the form of a
top-K
list, that is, the
K
items that our model predicts will have the
highest probability of the user liking them. This is done by computing the predicted score
for each item and ranking the list based on this score.
The exact method to perform this computation depends on the model involved. For ex-
ample, in user-based approaches, the ratings of similar users on items are used to compute
the recommendations for a user, while in an item-based approach, the computation is based
on the similarity of items the user has rated to the candidate items.
In matrix factorization, because we are modeling the ratings matrix directly, the predicted
score can be computed as the vector dot product between a user-factor vector and an item-
factor vector.
Generating movie recommendations from the MovieLens 100k dataset
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:
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:45
14/03/30 16:10:10 INFO DAGScheduler: Got job 30 (lookup at
MatrixFactorizationModel.scala:45) with 1 output partitions
(allowLocal=false)
...
14/03/30 16:10:10 INFO SparkContext: Job finished: lookup at
MatrixFactorizationModel.scala:46, took 0.023077 s
predictedRating: Double = 3.128545693368485
