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I'm using spark mllib for FP-Growth algorithm for our ML model.

Description of my issue:

I have taken transactional data from our production database to mine the frequent brought items recommendation. Now when I run the data with this algo I always get recommendations on the products that are sold the most. For ex. For a t-shirt I get recommendations of t-shits itself instead of something like pants etc..

Now one thing which I have Noticed is the item t-shirt in above example is present in dataset for 65k times and pant is appearing only in 2k cases.

how do I now optimise the data to get a better prediction?

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