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Collaborative Filtering - MLlib |
<a href="mllib-guide.html">MLlib</a> - Collaborative Filtering |
- Table of contents {:toc}
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) 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.
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. 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.
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".
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.
{% highlight scala %} import org.apache.spark.mllib.recommendation.ALS 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 = 20 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) {% 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 model = ALS.trainImplicit(ratings, rank, numIterations, alpha) {% endhighlight %}
{% 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 = 20;
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);
} } {% endhighlight %}
In order to run the above standalone application, follow the instructions provided in the Standalone Applications section of the Spark quick-start guide. Be sure to also include spark-mllib to your build file as a dependency.
{% highlight python %} from pyspark.mllib.recommendation import ALS from numpy import array
data = sc.textFile("data/mllib/als/test.data") ratings = data.map(lambda line: array([float(x) for x in line.split(',')]))
rank = 10 numIterations = 20 model = ALS.train(ratings, rank, numIterations)
testdata = ratings.map(lambda p: (int(p[0]), int(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).reduce(lambda x, y: x + y)/ratesAndPreds.count() print("Mean Squared Error = " + str(MSE)) {% 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 %}
model = ALS.trainImplicit(ratings, rank, numIterations, alpha = 0.01) {% endhighlight %}
The training exercises from the Spark Summit 2014 include a hands-on tutorial for personalized movie recommendation with MLlib.