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As always, an overfitting problem...
I'm trying to build a recommendation model but my model learns very well in train but in test the results decrease (yeah, overfitting).
My number of parameters were much higher than my number of data so I tried to do data augmentation but it didn't work. I tried to add L2 regularization, dropout and batchNorm but without any success.
Do you have any suggestions? What could explain this overfitting? How to counteract it?
I take all tracks to try. …!-->