Hands-on Exercises

In this chapter, we will use MLlib to make personalized movie recommendations tailored for you. We will work with 10 million ratings from 72,000 users on 10,000 movies, collected by MovieLens. This dataset is pre-loaded in the HDFS on your cluster in /movielens/large. For quick testing of your code, you may want to use a smaller dataset under /movielens/medium, which contains 1 million ratings from 6000 users on 4000 movies.

Data set

We will use two files from this MovieLens dataset: “ratings.dat” and “movies.dat”. All ratings are contained in the file “ratings.dat” and are in the following format:


Movie information is in the file “movies.dat” and is in the following format:


Collaborative filtering

Collaborative filtering is commonly used for recommender systems. These techniques aim to fill in the missing entries of a user-item association matrix, in our case, the user-movie rating matrix. MLlib currently supports model-based collaborative filtering, in which users and products are described by a small set of latent factors that can be used to predict missing entries. In particular, we implement the alternating least squares (ALS) algorithm to learn these latent factors.

Matrix Factorization


We will be using a standalone project template for this exercise. In your AMI, this has been setup in /root/machine-learning/scala/. You should find the following items in the directory.

  • sbt: Directory containing the SBT tool
  • build.sbt: SBT project file
  • MovieLensALS.scala: Main Scala program that you are going to edit, compile and run
  • solution: Directory containing the solution code
The main file you are going to edit, compile, and run for the exercises is MovieLensALS.scala. It should look as follows:
import java.util.Random

import org.apache.log4j.Logger
import org.apache.log4j.Level

import scala.io.Source

import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.SparkContext._
import org.apache.spark.rdd._
import org.apache.spark.mllib.recommendation.{ALS, Rating, MatrixFactorizationModel}

object MovieLensALS {

  def main(args: Array[String]) {


    if (args.length != 1) {
      println("Usage: sbt/sbt package \"run movieLensHomeDir\"")

    // set up environment

    val jarFile = "target/scala-2.10/movielens-als_2.10-0.0.jar"
    val sparkHome = "/root/spark"
    val master = Source.fromFile("/root/spark-ec2/cluster-url").mkString.trim
    val masterHostname = Source.fromFile("/root/spark-ec2/masters").mkString.trim
    val conf = new SparkConf()
      .set("spark.executor.memory", "8g")
    val sc = new SparkContext(conf)

    // load ratings and movie titles

    val movieLensHomeDir = "hdfs://" + masterHostname + ":9000" + args(0)

    val ratings = sc.textFile(movieLensHomeDir + "/ratings.dat").map { line =>
      val fields = line.split("::")
      // format: (timestamp % 10, Rating(userId, movieId, rating))
      (fields(3).toLong % 10, Rating(fields(0).toInt, fields(1).toInt, fields(2).toDouble))

    val movies = sc.textFile(movieLensHomeDir + "/movies.dat").map { line =>
      val fields = line.split("::")
      // format: (movieId, movieName)
      (fields(0).toInt, fields(1))

    // your code here

    // clean up


  /** Compute RMSE (Root Mean Squared Error). */
  def computeRmse(model: MatrixFactorizationModel, data: RDD[Rating], n: Long) = {
    // ...

  /** Elicitate ratings from command-line. */
  def elicitateRatings(movies: Seq[(Int, String)]) = {
    // ...

Let’s first take a closer look at our template code in a text editor on the cluster itself, then we’ll start adding code to the template. Locate the MovieLensALS class and open it with a text editor.

cd /root/machine-learning/scala
vim MovieLensALS.scala  # If you don't know vim, you can use emacs or nano

The cluster machines have vim, emacs, and nano installed for editing. Alternatively, you can use your favorite text editor locally and then copy-paste content into vim, emacs, or nano before running it.

For any Spark computation, we first create a SparkConf object and use it to create a Spark context object. For Scala or Java programs, we do that by providing the Spark cluster URL, the Spark home directory, and the JAR file that will be generated when we compile our program. For Python programs, we only need to provide the Spark cluster URL. Finally, we also name our program “MovieLensALS” to identify it in Spark’s web UI.

This is what it looks like in our template code:

    val conf = new SparkConf()
      .set("spark.executor.memory", "8g")
    val sc = new SparkContext(conf)

Next, the code uses the SparkContext to read in ratings. Recall that the rating file is a text file with “::” as the delimiter. The code parses each line to create a RDD for ratings that contains (Int, Rating) pairs. We only keep the last digit of the timestamp as a random key. The Rating class is a wrapper around tuple (user: Int, product: Int, rating: Double) defined in org.apache.spark.mllib.recommendation package.

    val movieLensHomeDir = "hdfs://" + masterHostname + ":9000" + args(0)

    val ratings = sc.textFile(movieLensHomeDir + "/ratings.dat").map { line =>
      val fields = line.split("::")
      // format: (timestamp % 10, Rating(userId, movieId, rating))
      (fields(3).toLong % 10, Rating(fields(0).toInt, fields(1).toInt, fields(2).toDouble))

Next, the code read in movie ids and titles, collect them into a movie id to title map.

    val movies = sc.textFile(movieLensHomeDir + "/movies.dat").map { line =>
      val fields = line.split("::")
      // format: (movieId, movieName)
      (fields(0).toInt, fields(1))

Now, let’s make our first edit to add code to get a summary of the ratings.

    val numRatings = ratings.count
    val numUsers = ratings.map(_._2.user).distinct.count
    val numMovies = ratings.map(_._2.product).distinct.count

    println("Got " + numRatings + " ratings from "
      + numUsers + " users on " + numMovies + " movies.")

Running the program

Before we compute movie recommendations, here is a quick reminder on how you can run the program at any point during this exercise. Save the MovieLensALS file run the following commands:

cd /root/machine-learning/scala
# change the folder name from "medium" to "large" to run on the large data set
sbt/sbt package "run /movielens/medium"

This command will compile the MovieLensALS class and create a JAR file in /root/machine-learning/scala/target/scala-2.10/. Finally, it will run the program. You should see output similar to the following on your screen:

Got 1000209 ratings from 6040 users on 3706 movies.

Rating elicitation

To make recommendation for you, we are going to learn your taste by asking you to rate a few movies. The movies should be popular ones to increase the chance of receiving ratings from you. To do this, we need to count ratings received for each movie and sort movies by rating counts. Then, take the top, e.g., 50, most rated movies and sample a small subset for rating elicitation.

    val mostRatedMovieIds = ratings.map(_._2.product) // extract movie ids
                                   .countByValue      // count ratings per movie
                                   .toSeq             // convert map to Seq
                                   .sortBy(- _._2)    // sort by rating count
                                   .take(50)          // take 50 most rated
                                   .map(_._1)         // get their ids
    val random = new Random(0)
    val selectedMovies = mostRatedMovieIds.filter(x => random.nextDouble() < 0.2)
                                          .map(x => (x, movies(x)))

Then for each of the selected movies, we will ask you to give a rating (1-5) or 0 if you have never watched this movie. The method eclicitateRatings returns your ratings, where you receive a special user id 0. The ratings are converted to a RDD[Rating] instance via sc.parallelize.

    val myRatings = elicitateRatings(selectedMovies)
    val myRatingsRDD = sc.parallelize(myRatings)

When you run the application, you should see prompt similar to the following:

Please rate the following movie (1-5 (best), or 0 if not seen):
Raiders of the Lost Ark (1981):

Splitting training data

We will use MLlib’s ALS to train a MatrixFactorizationModel, which takes a RDD[Rating] object as input. ALS has training parameters such as rank for matrix factors and regularization constants. To determine a good combination of the training parameters, we split the data into three non-overlapping subsets, named training, test, and validation, based on the last digit of the timestamp, and cache them. We will train multiple models based on the training set, select the best model on the validation set based on RMSE (Root Mean Squared Error), and finally evaluate the best model on the test set. We also add your ratings to the training set to make recommendations for you. We hold the training, validation, and test sets in memory by calling persist because we need to visit them multiple times.

    val numPartitions = 20
    val training = ratings.filter(x => x._1 < 6)
    val validation = ratings.filter(x => x._1 >= 6 && x._1 < 8)
    val test = ratings.filter(x => x._1 >= 8).values.persist

    val numTraining = training.count
    val numValidation = validation.count
    val numTest = test.count

    println("Training: " + numTraining + ", validation: " + numValidation + ", test: " + numTest)

After the split, you should see

Training: 602251, validation: 198919, test: 199049.

Training using ALS

In this section, we will use ALS.train to train a bunch of models, and select and evaluate the best. Among the training paramters of ALS, the most important ones are rank, lambda (regularization constant), and number of iterations. The train method of ALS we are going to use is defined as the following:

object ALS {

  def train(ratings: RDD[Rating], rank: Int, iterations: Int, lambda: Double)
    : MatrixFactorizationModel = {
    // ...

Ideally, we want to try a large number of combinations of them in order to find the best one. Due to time constraint, we will test only 8 combinations resulting from the cross product of 2 different ranks (8 and 12), 2 different lambdas (1.0 and 10.0), and two different numbers of iterations (10 and 20). We use the provided method computeRmse to compute the RMSE on the validation set for each model. The model with the smallest RMSE on the validation set becomes the one selected and its RMSE on the test set is used as the final metric.

    val ranks = List(8, 12)
    val lambdas = List(0.1, 10.0)
    val numIters = List(10, 20)
    var bestModel: Option[MatrixFactorizationModel] = None
    var bestValidationRmse = Double.MaxValue
    var bestRank = 0
    var bestLambda = -1.0
    var bestNumIter = -1
    for (rank <- ranks; lambda <- lambdas; numIter <- numIters) {
      val model = ALS.train(training, rank, numIter, lambda)
      val validationRmse = computeRmse(model, validation, numValidation)
      println("RMSE (validation) = " + validationRmse + " for the model trained with rank = "
        + rank + ", lambda = " + lambda + ", and numIter = " + numIter + ".")
      if (validationRmse < bestValidationRmse) {
        bestModel = Some(model)
        bestValidationRmse = validationRmse
        bestRank = rank
        bestLambda = lambda
        bestNumIter = numIter

    val testRmse = computeRmse(bestModel.get, test, numTest)

    println("The best model was trained with rank = " + bestRank + " and lambda = " + bestLambda
      + ", and numIter = " + bestNumIter + ", and its RMSE on the test set is " + testRmse + ".")

Spark might take a minute or two to train the models. You should see the following on the screen:

The best model was trained using rank 8 and lambda 10.0, and its RMSE on test is 0.8808492431998702.

Recommending movies for you

As the last part of our tutorial, let’s take a look at what movies our model recommends for you. This is done by generating (0, movieId) pairs for all movies you haven’t rated and calling the model’s predict method to get predictions. Recall that 0 is the special user id assigned to you.

class MatrixFactorizationModel {
  def predict(userProducts: RDD[(Int, Int)]): RDD[Rating] = {
    // ...

After we get all predictions, let us list the top 50 recommendations and see whether they look good to you.

    val myRatedMovieIds = myRatings.map(_.product).toSet
    val candidates = sc.parallelize(movies.keys.filter(!myRatedMovieIds.contains(_)).toSeq)
    val recommendations = bestModel.get
                                   .predict(candidates.map((0, _)))

    var i = 1
    println("Movies recommended for you:")
    recommendations.foreach { r =>
      println("%2d".format(i) + ": " + movies(r.product))
      i += 1

The output should be similar to

Movies recommended for you:
 1: Silence of the Lambs, The (1991)
 2: Saving Private Ryan (1998)
 3: Godfather, The (1972)
 4: Star Wars: Episode IV - A New Hope (1977)
 5: Braveheart (1995)
 6: Schindler's List (1993)
 7: Shawshank Redemption, The (1994)
 8: Star Wars: Episode V - The Empire Strikes Back (1980)
 9: Pulp Fiction (1994)
10: Alien (1979)

YMMV, and don’t expect to see movies from this decade, becaused the data set is old.


Comparing to a naive baseline

Does ALS output a non-trivial model? We can compare the evaluation result with a naive baseline model that only outputs the average rating (or you may try one that outputs the average rating per movie). Computing the baseline’s RMSE is straightforward:

    val meanRating = training.union(validation).map(_.rating).mean
    val baselineRmse = math.sqrt(test.map(x => (meanRating - x.rating) * (meanRating - x.rating))
                                     .reduce(_ + _) / numTest)
    val improvement = (baselineRmse - testRmse) / baselineRmse * 100
    println("The best model improves the baseline by " + "%1.2f".format(improvement) + "%.")

The output should be similar to

The best model improves the baseline by 20.96%.

It seems obvious that the trained model would outperform the naive baseline. However, a bad combination of training parameters would lead to a model worse than this naive baseline. Choosing the right set of parameters is quite important for this task.

Augmenting matrix factors

In this tutorial, we add your ratings to the training set. A better way to get the recommendations for you is training a matrix factorization model first and then augmenting the model using your ratings. If this sounds interesting to you, you can take a look at the implementation of MatrixFactorizationModel and see how to update the model for new users and new movies.

Solution code

In case you want to see your recommendation first or the complete source code, we put the solution under /root/machine-learning/scala/solution.

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Hands-on Exercises