Product recommendation based on behavior data of the client

Hello everyone
I am fairly new to Knime and we are using it in our classes. I would like to create a model that recommends the next product to the customer. To do this, I have a dataset that contains all the products a customer has bought every month for the last 1.5 years. The goal is to predict the next product he/she will buy.
Which node is best suited for this? Decision Tree, Random Forest, XGBoost or is there another option?
Thanks for the help

Hi @samuelegli

Welcome to the KNIME Community Forum.

Have a look at this page. Maybe it can inspire you.

Best regards


@samuelegli concerning product recommendations there are these links:

Question is if the purchases can be interpreted as a sequence then you could see if rule induction might work


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