docs/mllib-collaborative-filtering.md - spark - Git at Google

 ---
 layout: global
 title: Collaborative Filtering - MLlib
 displayTitle: <a href="mllib-guide.html">MLlib</a> - Collaborative Filtering
 ---

 * Table of contents
 {:toc}

 ## Collaborative filtering

 [Collaborative filtering](http://en.wikipedia.org/wiki/Recommender_system#Collaborative_filtering)
 is commonly used for recommender systems.  These techniques aim to fill in the
 missing entries of a user-item association 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.
 MLlib uses the [alternating least squares
 (ALS)](http://dl.acm.org/citation.cfm?id=1608614)
 algorithm to learn these latent factors. The implementation in MLlib has the
 following parameters:

 * *numBlocks* is the number of blocks used to parallelize computation (set to -1 to auto-configure).
 * *rank* is the number of latent factors in the model.
 * *iterations* is the number of iterations to run.
 * *lambda* specifies the regularization parameter in ALS.
 * *implicitPrefs* specifies whether to use the *explicit feedback* ALS variant or one adapted for
   *implicit feedback* data.
 * *alpha* is a parameter applicable to the implicit feedback variant of ALS that governs the
   *baseline* confidence in preference observations.

 ### Explicit vs. implicit feedback

 The standard approach to matrix factorization based collaborative filtering treats
 the entries in the user-item matrix as *explicit* preferences given by the user to the item.

 It is common in many real-world use cases to only have access to *implicit feedback* (e.g. views,
 clicks, purchases, likes, shares etc.). The approach used in MLlib to deal with such data is taken
 from
 [Collaborative Filtering for Implicit Feedback Datasets](http://dx.doi.org/10.1109/ICDM.2008.22).
 Essentially instead of trying to model the matrix of ratings directly, this approach treats the data
 as a combination of binary preferences and *confidence values*. The ratings are then related to the
 level of confidence in observed user preferences, rather than explicit ratings given to items.  The
 model then tries to find latent factors that can be used to predict the expected preference of a
 user for an item.

 ### Scaling of the regularization parameter

 Since v1.1, we scale the regularization parameter `lambda` in solving each least squares problem by
 the number of ratings the user generated in updating user factors,
 or the number of ratings the product received in updating product factors.
 This approach is named "ALS-WR" and discussed in the paper
 "[Large-Scale Parallel Collaborative Filtering for the Netflix Prize](http://dx.doi.org/10.1007/978-3-540-68880-8_32)".
 It makes `lambda` less dependent on the scale of the dataset.
 So we can apply the best parameter learned from a sampled subset to the full dataset
 and expect similar performance.

 ## Examples

 <div class="codetabs">

 <div data-lang="scala" markdown="1">
 In the following example we load rating data. Each row consists of a user, a product and a rating.
 We use the default [ALS.train()](api/scala/index.html#org.apache.spark.mllib.recommendation.ALS$)
 method which assumes ratings are explicit. We evaluate the
 recommendation model by measuring the Mean Squared Error of rating prediction.

 {% highlight scala %}
 import org.apache.spark.mllib.recommendation.ALS
 import org.apache.spark.mllib.recommendation.MatrixFactorizationModel
 import org.apache.spark.mllib.recommendation.Rating

 // Load and parse the data
 val data = sc.textFile("data/mllib/als/test.data")
 val ratings = data.map(_.split(',') match { case Array(user, item, rate) =>
     Rating(user.toInt, item.toInt, rate.toDouble)
   })

 // Build the recommendation model using ALS
 val rank = 10
 val numIterations = 10
 val model = ALS.train(ratings, rank, numIterations, 0.01)

 // Evaluate the model on rating data
 val usersProducts = ratings.map { case Rating(user, product, rate) =>
   (user, product)
 }
 val predictions =
   model.predict(usersProducts).map { case Rating(user, product, rate) =>
     ((user, product), rate)
   }
 val ratesAndPreds = ratings.map { case Rating(user, product, rate) =>
   ((user, product), rate)
 }.join(predictions)
 val MSE = ratesAndPreds.map { case ((user, product), (r1, r2)) =>
   val err = (r1 - r2)
   err * err
 }.mean()
 println("Mean Squared Error = " + MSE)

 // Save and load model
 model.save(sc, "myModelPath")
 val sameModel = MatrixFactorizationModel.load(sc, "myModelPath")
 {% endhighlight %}

 If the rating matrix is derived from another source of information (e.g., it is inferred from
 other signals), you can use the `trainImplicit` method to get better results.

 {% highlight scala %}
 val alpha = 0.01
 val lambda = 0.01
 val model = ALS.trainImplicit(ratings, rank, numIterations, lambda, alpha)
 {% endhighlight %}
 </div>

 <div data-lang="java" markdown="1">
 All of MLlib's methods use Java-friendly types, so you can import and call them there the same
 way you do in Scala. The only caveat is that the methods take Scala RDD objects, while the
 Spark Java API uses a separate `JavaRDD` class. You can convert a Java RDD to a Scala one by
 calling `.rdd()` on your `JavaRDD` object. A self-contained application example
 that is equivalent to the provided example in Scala is given bellow:

 {% highlight java %}
 import scala.Tuple2;

 import org.apache.spark.api.java.*;
 import org.apache.spark.api.java.function.Function;
 import org.apache.spark.mllib.recommendation.ALS;
 import org.apache.spark.mllib.recommendation.MatrixFactorizationModel;
 import org.apache.spark.mllib.recommendation.Rating;
 import org.apache.spark.SparkConf;

 public class CollaborativeFiltering {
   public static void main(String[] args) {
     SparkConf conf = new SparkConf().setAppName("Collaborative Filtering Example");
     JavaSparkContext sc = new JavaSparkContext(conf);

     // Load and parse the data
     String path = "data/mllib/als/test.data";
     JavaRDD<String> data = sc.textFile(path);
     JavaRDD<Rating> ratings = data.map(
       new Function<String, Rating>() {
         public Rating call(String s) {
           String[] sarray = s.split(",");
           return new Rating(Integer.parseInt(sarray[0]), Integer.parseInt(sarray[1]),
                             Double.parseDouble(sarray[2]));
         }
       }
     );

     // Build the recommendation model using ALS
     int rank = 10;
     int numIterations = 10;
     MatrixFactorizationModel model = ALS.train(JavaRDD.toRDD(ratings), rank, numIterations, 0.01);

     // Evaluate the model on rating data
     JavaRDD<Tuple2<Object, Object>> userProducts = ratings.map(
       new Function<Rating, Tuple2<Object, Object>>() {
         public Tuple2<Object, Object> call(Rating r) {
           return new Tuple2<Object, Object>(r.user(), r.product());
         }
       }
     );
     JavaPairRDD<Tuple2<Integer, Integer>, Double> predictions = JavaPairRDD.fromJavaRDD(
       model.predict(JavaRDD.toRDD(userProducts)).toJavaRDD().map(
         new Function<Rating, Tuple2<Tuple2<Integer, Integer>, Double>>() {
           public Tuple2<Tuple2<Integer, Integer>, Double> call(Rating r){
             return new Tuple2<Tuple2<Integer, Integer>, Double>(
               new Tuple2<Integer, Integer>(r.user(), r.product()), r.rating());
           }
         }
     ));
     JavaRDD<Tuple2<Double, Double>> ratesAndPreds =
       JavaPairRDD.fromJavaRDD(ratings.map(
         new Function<Rating, Tuple2<Tuple2<Integer, Integer>, Double>>() {
           public Tuple2<Tuple2<Integer, Integer>, Double> call(Rating r){
             return new Tuple2<Tuple2<Integer, Integer>, Double>(
               new Tuple2<Integer, Integer>(r.user(), r.product()), r.rating());
           }
         }
     )).join(predictions).values();
     double MSE = JavaDoubleRDD.fromRDD(ratesAndPreds.map(
       new Function<Tuple2<Double, Double>, Object>() {
         public Object call(Tuple2<Double, Double> pair) {
           Double err = pair._1() - pair._2();
           return err * err;
         }
       }
     ).rdd()).mean();
     System.out.println("Mean Squared Error = " + MSE);

     // Save and load model
     model.save(sc.sc(), "myModelPath");
     MatrixFactorizationModel sameModel = MatrixFactorizationModel.load(sc.sc(), "myModelPath");
   }
 }
 {% endhighlight %}
 </div>

 <div data-lang="python" markdown="1">
 In the following example we load rating data. Each row consists of a user, a product and a rating.
 We use the default ALS.train() method which assumes ratings are explicit. We evaluate the
 recommendation by measuring the Mean Squared Error of rating prediction.

 {% highlight python %}
 from pyspark.mllib.recommendation import ALS, MatrixFactorizationModel, Rating

 # Load and parse the data
 data = sc.textFile("data/mllib/als/test.data")
 ratings = data.map(lambda l: l.split(',')).map(lambda l: Rating(int(l[0]), int(l[1]), float(l[2])))

 # Build the recommendation model using Alternating Least Squares
 rank = 10
 numIterations = 10
 model = ALS.train(ratings, rank, numIterations)

 # Evaluate the model on training data
 testdata = ratings.map(lambda p: (p[0], p[1]))
 predictions = model.predictAll(testdata).map(lambda r: ((r[0], r[1]), r[2]))
 ratesAndPreds = ratings.map(lambda r: ((r[0], r[1]), r[2])).join(predictions)
 MSE = ratesAndPreds.map(lambda r: (r[1][0] - r[1][1])**2).mean()
 print("Mean Squared Error = " + str(MSE))

 # Save and load model
 model.save(sc, "myModelPath")
 sameModel = MatrixFactorizationModel.load(sc, "myModelPath")
 {% endhighlight %}

 If the rating matrix is derived from other source of information (i.e., it is inferred from other
 signals), you can use the trainImplicit method to get better results.

 {% highlight python %}
 # Build the recommendation model using Alternating Least Squares based on implicit ratings
 model = ALS.trainImplicit(ratings, rank, numIterations, alpha=0.01)
 {% endhighlight %}
 </div>

 </div>

 In order to run the above application, follow the instructions
 provided in the [Self-Contained Applications](quick-start.html#self-contained-applications)
 section of the Spark
 Quick Start guide. Be sure to also include *spark-mllib* to your build file as
 a dependency.

 ## Tutorial

 The [training exercises](https://databricks-training.s3.amazonaws.com/index.html) from the Spark Summit 2014 include a hands-on tutorial for
 [personalized movie recommendation with MLlib](https://databricks-training.s3.amazonaws.com/movie-recommendation-with-mllib.html).
	---
	layout: global
	title: Collaborative Filtering - MLlib
	displayTitle: <a href="mllib-guide.html">MLlib</a> - Collaborative Filtering
	---

	* Table of contents
	{:toc}

	## Collaborative filtering

	[Collaborative filtering](http://en.wikipedia.org/wiki/Recommender_system#Collaborative_filtering)
	is commonly used for recommender systems. These techniques aim to fill in the
	missing entries of a user-item association 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.
	MLlib uses the [alternating least squares
	(ALS)](http://dl.acm.org/citation.cfm?id=1608614)
	algorithm to learn these latent factors. The implementation in MLlib has the
	following parameters:

	* numBlocks is the number of blocks used to parallelize computation (set to -1 to auto-configure).
	* rank is the number of latent factors in the model.
	* iterations is the number of iterations to run.
	* lambda specifies the regularization parameter in ALS.
	* implicitPrefs specifies whether to use the explicit feedback ALS variant or one adapted for
	implicit feedback data.
	* alpha is a parameter applicable to the implicit feedback variant of ALS that governs the
	baseline confidence in preference observations.

	### Explicit vs. implicit feedback

	The standard approach to matrix factorization based collaborative filtering treats
	the entries in the user-item matrix as explicit preferences given by the user to the item.

	It is common in many real-world use cases to only have access to implicit feedback (e.g. views,
	clicks, purchases, likes, shares etc.). The approach used in MLlib to deal with such data is taken
	from
	[Collaborative Filtering for Implicit Feedback Datasets](http://dx.doi.org/10.1109/ICDM.2008.22).
	Essentially instead of trying to model the matrix of ratings directly, this approach treats the data
	as a combination of binary preferences and confidence values. The ratings are then related to the
	level of confidence in observed user preferences, rather than explicit ratings given to items. The
	model then tries to find latent factors that can be used to predict the expected preference of a
	user for an item.

	### Scaling of the regularization parameter

	Since v1.1, we scale the regularization parameter `lambda` in solving each least squares problem by
	the number of ratings the user generated in updating user factors,
	or the number of ratings the product received in updating product factors.
	This approach is named "ALS-WR" and discussed in the paper
	"[Large-Scale Parallel Collaborative Filtering for the Netflix Prize](http://dx.doi.org/10.1007/978-3-540-68880-8_32)".
	It makes `lambda` less dependent on the scale of the dataset.
	So we can apply the best parameter learned from a sampled subset to the full dataset
	and expect similar performance.

	## Examples

	<div class="codetabs">

	<div data-lang="scala" markdown="1">
	In the following example we load rating data. Each row consists of a user, a product and a rating.
	We use the default [ALS.train()](api/scala/index.html#org.apache.spark.mllib.recommendation.ALS$)
	method which assumes ratings are explicit. We evaluate the
	recommendation model by measuring the Mean Squared Error of rating prediction.

	{% highlight scala %}
	import org.apache.spark.mllib.recommendation.ALS
	import org.apache.spark.mllib.recommendation.MatrixFactorizationModel
	import org.apache.spark.mllib.recommendation.Rating

	// Load and parse the data
	val data = sc.textFile("data/mllib/als/test.data")
	val ratings = data.map(_.split(',') match { case Array(user, item, rate) =>
	Rating(user.toInt, item.toInt, rate.toDouble)
	})

	// Build the recommendation model using ALS
	val rank = 10
	val numIterations = 10
	val model = ALS.train(ratings, rank, numIterations, 0.01)

	// Evaluate the model on rating data
	val usersProducts = ratings.map { case Rating(user, product, rate) =>
	(user, product)
	}
	val predictions =
	model.predict(usersProducts).map { case Rating(user, product, rate) =>
	((user, product), rate)
	}
	val ratesAndPreds = ratings.map { case Rating(user, product, rate) =>
	((user, product), rate)
	}.join(predictions)
	val MSE = ratesAndPreds.map { case ((user, product), (r1, r2)) =>
	val err = (r1 - r2)
	err * err
	}.mean()
	println("Mean Squared Error = " + MSE)

	// Save and load model
	model.save(sc, "myModelPath")
	val sameModel = MatrixFactorizationModel.load(sc, "myModelPath")
	{% endhighlight %}

	If the rating matrix is derived from another source of information (e.g., it is inferred from
	other signals), you can use the `trainImplicit` method to get better results.

	{% highlight scala %}
	val alpha = 0.01
	val lambda = 0.01
	val model = ALS.trainImplicit(ratings, rank, numIterations, lambda, alpha)
	{% endhighlight %}
	</div>

	<div data-lang="java" markdown="1">
	All of MLlib's methods use Java-friendly types, so you can import and call them there the same
	way you do in Scala. The only caveat is that the methods take Scala RDD objects, while the
	Spark Java API uses a separate `JavaRDD` class. You can convert a Java RDD to a Scala one by
	calling `.rdd()` on your `JavaRDD` object. A self-contained application example
	that is equivalent to the provided example in Scala is given bellow:

	{% highlight java %}
	import scala.Tuple2;

	import org.apache.spark.api.java.*;
	import org.apache.spark.api.java.function.Function;
	import org.apache.spark.mllib.recommendation.ALS;
	import org.apache.spark.mllib.recommendation.MatrixFactorizationModel;
	import org.apache.spark.mllib.recommendation.Rating;
	import org.apache.spark.SparkConf;

	public class CollaborativeFiltering {
	public static void main(String[] args) {
	SparkConf conf = new SparkConf().setAppName("Collaborative Filtering Example");
	JavaSparkContext sc = new JavaSparkContext(conf);

	// Load and parse the data
	String path = "data/mllib/als/test.data";
	JavaRDD<String> data = sc.textFile(path);
	JavaRDD<Rating> ratings = data.map(
	new Function<String, Rating>() {
	public Rating call(String s) {
	String[] sarray = s.split(",");
	return new Rating(Integer.parseInt(sarray[0]), Integer.parseInt(sarray[1]),
	Double.parseDouble(sarray[2]));
	}
	}
	);

	// Build the recommendation model using ALS
	int rank = 10;
	int numIterations = 10;
	MatrixFactorizationModel model = ALS.train(JavaRDD.toRDD(ratings), rank, numIterations, 0.01);

	// Evaluate the model on rating data
	JavaRDD<Tuple2<Object, Object>> userProducts = ratings.map(
	new Function<Rating, Tuple2<Object, Object>>() {
	public Tuple2<Object, Object> call(Rating r) {
	return new Tuple2<Object, Object>(r.user(), r.product());
	}
	}
	);
	JavaPairRDD<Tuple2<Integer, Integer>, Double> predictions = JavaPairRDD.fromJavaRDD(
	model.predict(JavaRDD.toRDD(userProducts)).toJavaRDD().map(
	new Function<Rating, Tuple2<Tuple2<Integer, Integer>, Double>>() {
	public Tuple2<Tuple2<Integer, Integer>, Double> call(Rating r){
	return new Tuple2<Tuple2<Integer, Integer>, Double>(
	new Tuple2<Integer, Integer>(r.user(), r.product()), r.rating());
	}
	}
	));
	JavaRDD<Tuple2<Double, Double>> ratesAndPreds =
	JavaPairRDD.fromJavaRDD(ratings.map(
	new Function<Rating, Tuple2<Tuple2<Integer, Integer>, Double>>() {
	public Tuple2<Tuple2<Integer, Integer>, Double> call(Rating r){
	return new Tuple2<Tuple2<Integer, Integer>, Double>(
	new Tuple2<Integer, Integer>(r.user(), r.product()), r.rating());
	}
	}
	)).join(predictions).values();
	double MSE = JavaDoubleRDD.fromRDD(ratesAndPreds.map(
	new Function<Tuple2<Double, Double>, Object>() {
	public Object call(Tuple2<Double, Double> pair) {
	Double err = pair._1() - pair._2();
	return err * err;
	}
	}
	).rdd()).mean();
	System.out.println("Mean Squared Error = " + MSE);

	// Save and load model
	model.save(sc.sc(), "myModelPath");
	MatrixFactorizationModel sameModel = MatrixFactorizationModel.load(sc.sc(), "myModelPath");
	}
	}
	{% endhighlight %}
	</div>

	<div data-lang="python" markdown="1">
	In the following example we load rating data. Each row consists of a user, a product and a rating.
	We use the default ALS.train() method which assumes ratings are explicit. We evaluate the
	recommendation by measuring the Mean Squared Error of rating prediction.

	{% highlight python %}
	from pyspark.mllib.recommendation import ALS, MatrixFactorizationModel, Rating

	# Load and parse the data
	data = sc.textFile("data/mllib/als/test.data")
	ratings = data.map(lambda l: l.split(',')).map(lambda l: Rating(int(l[0]), int(l[1]), float(l[2])))

	# Build the recommendation model using Alternating Least Squares
	rank = 10
	numIterations = 10
	model = ALS.train(ratings, rank, numIterations)

	# Evaluate the model on training data
	testdata = ratings.map(lambda p: (p[0], p[1]))
	predictions = model.predictAll(testdata).map(lambda r: ((r[0], r[1]), r[2]))
	ratesAndPreds = ratings.map(lambda r: ((r[0], r[1]), r[2])).join(predictions)
	MSE = ratesAndPreds.map(lambda r: (r[1][0] - r[1][1])**2).mean()
	print("Mean Squared Error = " + str(MSE))

	# Save and load model
	model.save(sc, "myModelPath")
	sameModel = MatrixFactorizationModel.load(sc, "myModelPath")
	{% endhighlight %}

	If the rating matrix is derived from other source of information (i.e., it is inferred from other
	signals), you can use the trainImplicit method to get better results.

	{% highlight python %}
	# Build the recommendation model using Alternating Least Squares based on implicit ratings
	model = ALS.trainImplicit(ratings, rank, numIterations, alpha=0.01)
	{% endhighlight %}
	</div>

	</div>

	In order to run the above application, follow the instructions
	provided in the [Self-Contained Applications](quick-start.html#self-contained-applications)
	section of the Spark
	Quick Start guide. Be sure to also include spark-mllib to your build file as
	a dependency.

	## Tutorial

	The [training exercises](https://databricks-training.s3.amazonaws.com/index.html) from the Spark Summit 2014 include a hands-on tutorial for
	[personalized movie recommendation with MLlib](https://databricks-training.s3.amazonaws.com/movie-recommendation-with-mllib.html).