Tuning parameters for implicit pyspark.ml ALS matrix factorization model through pyspark.ml CrossValidator

Question

I\'m trying to tune the parameters of an ALS matrix factorization model that uses implicit data. For this, I\'m trying to use pyspark.ml.tuning.CrossValidator to run through

Answer 1

With implicit feedbacks we don't have user reactions to our recommendations. Thus, we cannot use precision based metrics.

In the already cited paper, the expected percentile ranking metric is used instead.

You can try to implement an Evaluator based on a similar metric in the Spark ML lib, and use it in your Cross Validation pipeline.

Answer 2

Very late to the party here, but I'll post in case anyone stumbles upon this question like I did.

I was getting a similar error when trying to use CrossValidator with an ALS model. I resolved it by setting the coldStartStrategy parameter in ALS to "drop". That is:

alsImplicit = ALS(implicitPrefs=True, coldStartStrategy="drop")

and keep the rest of the code the same.

I expect what was happening in my example is that the cross-validation splits created scenarios where I had items in the validation set that did not appear in the training set, which results in NaN prediction values. The best solution is to drop the NaN values when evaluating, as described in the documentation.

I don't know if we were getting the same error so can't guarantee this would solve OP's problem, but it's good practice to set coldStartStrategy="drop" for cross validation anyway.

Note: my error message was "Params must be either a param map or a list/tuple of param maps", which didn't seem to imply an issue with the coldStartStrategy parameter or NaN values but this solution resolved the error.

Answer 3

Ignoring technical issues, strictly speaking neither method is correct given the input generated by ALS with implicit feedback.

• you cannot use RegressionEvaluator because, as you already know, prediction can be interpreted as a confidence value and is represented as a floating point number in range [0, 1] and label column is just an unbound integer. These values are clearly not comparable.
• you cannot use BinaryClassificationEvaluator because even if the prediction can be interpreted as probability label doesn't represent binary decision. Moreover prediction column has invalid type and couldn't be used directly with BinaryClassificationEvaluator

You can try to convert one of the columns so input fit the requirements but this is is not really a justified approach from a theoretical perspective and introduces additional parameters which are hard to tune.

• map label column to [0, 1] range and use RMSE.

• convert label column to binary indicator with fixed threshold and extend ALS / ALSModel to return expected column type. Assuming threshold value is 1 it could be something like this

  from pyspark.ml.recommendation import *
  from pyspark.sql.functions import udf, col
  from pyspark.mllib.linalg import DenseVector, VectorUDT
  
  class BinaryALS(ALS):
      def fit(self, df):
          assert self.getImplicitPrefs()
          model = super(BinaryALS, self).fit(df)
          return ALSBinaryModel(model._java_obj)
  
  class ALSBinaryModel(ALSModel):
      def transform(self, df):
          transformed = super(ALSBinaryModel, self).transform(df)
          as_vector = udf(lambda x: DenseVector([1 - x, x]), VectorUDT())
          return transformed.withColumn(
              "rawPrediction", as_vector(col("prediction")))
  
  # Add binary label column
  with_binary = dfCounts.withColumn(
      "label_binary", (col("rating") > 0).cast("double"))
  
  als_binary_model = BinaryALS(implicitPrefs=True).fit(with_binary)
  
  evaluatorB = BinaryClassificationEvaluator(
      metricName="areaUnderROC", labelCol="label_binary")
  
  evaluatorB.evaluate(als_binary_model.transform(with_binary))
  ## 1.0
  

Generally speaking, material about evaluating recommender systems with implicit feedbacks is kind of missing in textbooks, I suggest you take a read on eliasah's answer about evaluating these kind of recommenders.