I have two data sets with transaction history of customers by date and product (de-identified). These are from two different sources and have different capture rate (e.g.: One might have 5 transactions for a customer and the other may report 7). I also have information like ZIP, age, gender for these customers.
What machine learning algorithm can I leverage to find same customers across the sources based on similarity in transnational history?
I was thinking if somehow collaborative filtering may be of help here.
Thanks in advance!