path: root/docs/mllib-collaborative-filtering.md

---
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).