Deep Learning models have been gaining increasing attention in the recommendation systems community, replacing some of the traditional methods. The sparse nature of the problems and the different inputs types offer unique challenges for feature engineering and architecture planning, in order to balance between memorization and generalization.

During the past 2 years the algorithms team in Taboola moved all of our algorithms to DL. In this talk we'll share the lessons we learned doing so. We'll talk about building NN with multiple input types (click history, text and pictures); feature engineering in DL; capturing interactions between features; and the way modelling decisions are related to system engineering and research culture.