This document discusses personalization using big data and collaborative filtering techniques. It describes how collaborative filtering works by analyzing user preferences for items to predict other items a user may like. Several collaborative filtering algorithms are presented, including item-based algorithms that recommend similar items to those a user has rated highly. Matrix math and Apache Mahout are introduced as ways to implement collaborative filtering at scale using Hadoop. Real-world examples of using these techniques for recommendations are also briefly mentioned.