There are a lot of approaches you can take, one of the fun things about graph-ML is the number of different solutions. You could try:

• Extending the Cypher query to extract more node/relationship information into the tabular data consumed by Keras
• Perform collaborative filtering (see our TensorFlow article!)
• Look at how users browse/consume products and use those histories to embed the products into a similarity space