Apache Spark, ALS Recomendation example in documentation has a extra column I dont know its use

Question

In the ALS example I have the following code:

(http://spark.apache.org/docs/latest/ml-collaborative-filtering.html)

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 you see it Creates a Row with the attribute timestamp, but then int the ALS creation it doesn't use it.

What is the purpose of the attribute timestamp in the Row?

Answer 1

None. It is just one of the fields that come with MovieLens data. For ALS it has no use and you can ignore it.