An open-source deep learning architecture for recommendation systems that combines dense and sparse features through embedding tables, interaction layers, and MLP components, used for CTR prediction and personalization.
A supervised learning implementation of factorization machines for classification, regression, and ranking that captures feature interactions, widely used in recommendation systems.