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.