spark-instrumented-optimizer/docs/mllib-collaborative-filtering.md

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

• Table of contents {:toc}

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. 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. 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 our 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. 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.

Examples

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.Rating

// Load and parse the data val data = sc.textFile("mllib/data/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 other source of information (i.e., 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 %}

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.

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 from numpy import array

Load and parse the data

data = sc.textFile("mllib/data/als/test.data") ratings = data.map(lambda line: array([float(x) for x in line.split(',')]))

Build the recommendation model using Alternating Least Squares

rank = 10 numIterations = 20 model = ALS.train(ratings, rank, numIterations)

Evaluate the model on training data

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 %}

Build the recommendation model using Alternating Least Squares based on implicit ratings

model = ALS.trainImplicit(ratings, rank, numIterations, alpha = 0.01) {% endhighlight %}

Tutorial

AMP Camp provides a hands-on tutorial for personalized movie recommendation with MLlib.