Predictive AI is often associated with product recommenders. We present a landscape of multi-domain behavioral models that predict multi-modal user preferences and behavior. This session will take the audience from first principles of the new Correlated Cross-Occurrence (CCO) algorithms showing the important innovations that lead to new ways to predict behavior into a deep dive into as variety different use cases, for instance using dislikes to predict likes, using search terms to predict purchase, and using conversion to augment search indexes with behavioral data to produce behavioral search. Some of these are nearly impossible to address without this new technique. We show the tensor algebra that makes up the landscape. Next, we walk through the computation using real-world data. Finally, we show how Mahout's generalized CPU/GPU integration and recently added CUDA support bring significant reductions in time and cost to calculate the CCO models. We expect the audience to come away with an understanding of the kind of applications to be built CCO and how to do so in performant in cost reducing ways.