Synthetic Minority Over-sampling Technique SMOTE, is a well known method to tackle imbalanced datasets. There are many papers with a lot of citations out-there claiming that it is used to boost accuracy in unbalanced data scenarios.
But then, when I see Kaggle competitions, it is rarely used, to the best of my knowledge there are no prize-winning Kaggle/ML competitions where it is used to achieve the best solution. Why SMOTE is not used in Kaggle?
I even see applied research papers (where there are millions of $ at stake) that SMOTE is not used: Practical Lessons from Predicting Clicks on Ads at Facebook
Is this because it's not the best strategy possible? Is it a research niche with no optimal real-life application? Is there any ML competition with a high-reward where this was used to achieve the best solution?
I guess I am just hesitant that creating synthetic data actually helps.