This sounds very exciting! I'm interested in the A/B tests you ran to show revenue lift from your recommendations. What is the baseline model you test against? It seems to me that the most "fair" comparison would be to set up exactly the same vector representations and neural network model for only a single company at a time, and compare the performance to demonstrate that it is really your approach of combining different companies' datasets that provides the extra value here. Is that what you guys did?

In A/B tests we have compared against e-commerce stores existing recommendations which are either manual or made by other recommender systems. We will consider running experiments like the one you describe it is a good idea!