Using ALS Matrix Factorization for Making Recommendations in Spark

ALS Matrix Factorization in Spark.ipynb

"Suprisingly, the algorithm gets even better evaluation scores when tested on all-unseen data! This means that the model can be underfitting"

This, I think, is not the correct interpretation.
When MPR is being computed, we rank the predictions. The training samples (userId x genreId) will get a higher prediction value than the test sample (userId x genreId).
So if we rank the whole dataset and then evaluate MPR by tagging test samples as 1, we will get a worse MPR than when we rank after removing the training samples: this is because in the first scenario, test samples will have worse rank than train samples and hence a worse MPR

@hellfire2310 good point.

Hi,

I'm trying to understand how did you implement MPR but I am a little bit confused. Could you please give the pseudo code for MPR?. I will implement for my binary only dataset with PySpark ALS. Thank in advance.

Best wishes,
Ahmet

@ahmetlekesiz You can find some explanation here https://stats.stackexchange.com/questions/460166/mean-percentage-ranking-in-implicit-feedback-als I agree that the Spark implementation is not the most readable way to do this. Check also the links I provided in the notebook.