April 2017
Intermediate to advanced
532 pages
12h 39m
English
book
Machine Learning with Spark - Second Edition
by Rajdeep Dua, Brian O'Neill, Stephen Boesch, Manpreet Singh Ghotra, Nick Pentreath
Content preview from Machine Learning with Spark - Second Edition
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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 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 ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.