# Collaborative Filtering

## 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. `spark.ml`

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.
`spark.ml`

uses the alternating least squares
(ALS)
algorithm to learn these latent factors. The implementation in `spark.ml`

has the
following parameters:

*numBlocks*is the number of blocks the users and items will be partitioned into in order to parallelize computation (defaults to 10).*rank*is the number of latent factors in the model (defaults to 10).*maxIter*is the maximum number of iterations to run (defaults to 10).*regParam*specifies the regularization parameter in ALS (defaults to 1.0).*implicitPrefs*specifies whether to use the*explicit feedback*ALS variant or one adapted for*implicit feedback*data (defaults to`false`

which means using*explicit feedback*).*alpha*is a parameter applicable to the implicit feedback variant of ALS that governs the*baseline*confidence in preference observations (defaults to 1.0).*nonnegative*specifies whether or not to use nonnegative constraints for least squares (defaults to`false`

).

**Note:** The DataFrame-based API for ALS currently only supports integers for user and item ids.
Other numeric types are supported for the user and item id columns,
but the ids must be within the integer value range.

### 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,
for example, users giving ratings to movies.

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 `spark.ml`

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 numbers representing the *strength* in observations of user actions (such as the number of clicks,
or the cumulative duration someone spent viewing a movie). Those numbers 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

We scale the regularization parameter `regParam`

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 `regParam`

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

In the following example, we load ratings data from the
MovieLens dataset, each row
consisting of a user, a movie, a rating and a timestamp.
We then train an ALS model which assumes, by default, that the ratings are
explicit (`implicitPrefs`

is `false`

).
We evaluate the recommendation model by measuring the root-mean-square error of
rating prediction.

Refer to the `ALS`

Scala docs
for more details on the API.

```
import org.apache.spark.ml.evaluation.RegressionEvaluator
import org.apache.spark.ml.recommendation.ALS
case class Rating(userId: Int, movieId: Int, rating: Float, timestamp: Long)
def parseRating(str: String): Rating = {
val fields = str.split("::")
assert(fields.size == 4)
Rating(fields(0).toInt, fields(1).toInt, fields(2).toFloat, fields(3).toLong)
}
val ratings = spark.read.textFile("data/mllib/als/sample_movielens_ratings.txt")
.map(parseRating)
.toDF()
val Array(training, test) = ratings.randomSplit(Array(0.8, 0.2))
// Build the recommendation model using ALS on the training data
val als = new ALS()
.setMaxIter(5)
.setRegParam(0.01)
.setUserCol("userId")
.setItemCol("movieId")
.setRatingCol("rating")
val model = als.fit(training)
// Evaluate the model by computing the RMSE on the test data
val predictions = model.transform(test)
val evaluator = new RegressionEvaluator()
.setMetricName("rmse")
.setLabelCol("rating")
.setPredictionCol("prediction")
val rmse = evaluator.evaluate(predictions)
println(s"Root-mean-square error = $rmse")
```

If the rating matrix is derived from another source of information (i.e. it is
inferred from other signals), you can set `implicitPrefs`

to `true`

to get
better results:

```
val als = new ALS()
.setMaxIter(5)
.setRegParam(0.01)
.setImplicitPrefs(true)
.setUserCol("userId")
.setItemCol("movieId")
.setRatingCol("rating")
```

In the following example, we load ratings data from the
MovieLens dataset, each row
consisting of a user, a movie, a rating and a timestamp.
We then train an ALS model which assumes, by default, that the ratings are
explicit (`implicitPrefs`

is `false`

).
We evaluate the recommendation model by measuring the root-mean-square error of
rating prediction.

Refer to the `ALS`

Java docs
for more details on the API.

```
import java.io.Serializable;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.ml.evaluation.RegressionEvaluator;
import org.apache.spark.ml.recommendation.ALS;
import org.apache.spark.ml.recommendation.ALSModel;
public static class Rating implements Serializable {
private int userId;
private int movieId;
private float rating;
private long timestamp;
public Rating() {}
public Rating(int userId, int movieId, float rating, long timestamp) {
this.userId = userId;
this.movieId = movieId;
this.rating = rating;
this.timestamp = timestamp;
}
public int getUserId() {
return userId;
}
public int getMovieId() {
return movieId;
}
public float getRating() {
return rating;
}
public long getTimestamp() {
return timestamp;
}
public static Rating parseRating(String str) {
String[] fields = str.split("::");
if (fields.length != 4) {
throw new IllegalArgumentException("Each line must contain 4 fields");
}
int userId = Integer.parseInt(fields[0]);
int movieId = Integer.parseInt(fields[1]);
float rating = Float.parseFloat(fields[2]);
long timestamp = Long.parseLong(fields[3]);
return new Rating(userId, movieId, rating, timestamp);
}
}
JavaRDD<Rating> ratingsRDD = spark
.read().textFile("data/mllib/als/sample_movielens_ratings.txt").javaRDD()
.map(new Function<String, Rating>() {
public Rating call(String str) {
return Rating.parseRating(str);
}
});
Dataset<Row> ratings = spark.createDataFrame(ratingsRDD, Rating.class);
Dataset<Row>[] splits = ratings.randomSplit(new double[]{0.8, 0.2});
Dataset<Row> training = splits[0];
Dataset<Row> test = splits[1];
// Build the recommendation model using ALS on the training data
ALS als = new ALS()
.setMaxIter(5)
.setRegParam(0.01)
.setUserCol("userId")
.setItemCol("movieId")
.setRatingCol("rating");
ALSModel model = als.fit(training);
// Evaluate the model by computing the RMSE on the test data
Dataset<Row> predictions = model.transform(test);
RegressionEvaluator evaluator = new RegressionEvaluator()
.setMetricName("rmse")
.setLabelCol("rating")
.setPredictionCol("prediction");
Double rmse = evaluator.evaluate(predictions);
System.out.println("Root-mean-square error = " + rmse);
```

If the rating matrix is derived from another source of information (i.e. it is
inferred from other signals), you can set `implicitPrefs`

to `true`

to get
better results:

```
ALS als = new ALS()
.setMaxIter(5)
.setRegParam(0.01)
.setImplicitPrefs(true)
.setUserCol("userId")
.setItemCol("movieId")
.setRatingCol("rating");
```

In the following example, we load ratings data from the
MovieLens dataset, each row
consisting of a user, a movie, a rating and a timestamp.
We then train an ALS model which assumes, by default, that the ratings are
explicit (`implicitPrefs`

is `False`

).
We evaluate the recommendation model by measuring the root-mean-square error of
rating prediction.

Refer to the `ALS`

Python docs
for more details on the API.

```
from pyspark.ml.evaluation import RegressionEvaluator
from pyspark.ml.recommendation import ALS
from pyspark.sql import Row
lines = spark.read.text("data/mllib/als/sample_movielens_ratings.txt").rdd
parts = lines.map(lambda row: row.value.split("::"))
ratingsRDD = parts.map(lambda p: Row(userId=int(p[0]), movieId=int(p[1]),
rating=float(p[2]), timestamp=long(p[3])))
ratings = spark.createDataFrame(ratingsRDD)
(training, test) = ratings.randomSplit([0.8, 0.2])
# Build the recommendation model using ALS on the training data
als = ALS(maxIter=5, regParam=0.01, userCol="userId", itemCol="movieId", ratingCol="rating")
model = als.fit(training)
# Evaluate the model by computing the RMSE on the test data
predictions = model.transform(test)
evaluator = RegressionEvaluator(metricName="rmse", labelCol="rating",
predictionCol="prediction")
rmse = evaluator.evaluate(predictions)
print("Root-mean-square error = " + str(rmse))
```

If the rating matrix is derived from another source of information (i.e. it is
inferred from other signals), you can set `implicitPrefs`

to `True`

to get
better results:

```
als = ALS(maxIter=5, regParam=0.01, implicitPrefs=True,
userCol="userId", itemCol="movieId", ratingCol="rating")
```

Refer to the R API docs for more details.

```
# Load training data
data <- list(list(0, 0, 4.0), list(0, 1, 2.0), list(1, 1, 3.0),
list(1, 2, 4.0), list(2, 1, 1.0), list(2, 2, 5.0))
df <- createDataFrame(data, c("userId", "movieId", "rating"))
training <- df
test <- df
# Fit a recommendation model using ALS with spark.als
model <- spark.als(training, maxIter = 5, regParam = 0.01, userCol = "userId",
itemCol = "movieId", ratingCol = "rating")
# Model summary
summary(model)
# Prediction
predictions <- predict(model, test)
showDF(predictions)
```