This document discusses using big data and machine learning techniques on AWS for content recommendations. It describes three common approaches: search with boosting which adjusts search rankings based on popularity signals; collaborative filtering which identifies similar users and items; and neural networks which use historical user events to create a model that predicts favorites. It also introduces Amazon DSSTNE (Deep Scalable Sparse Tensor Network Engine) for automating GPU-accelerated training and prediction at scale for recommendation systems.