Netflix Top Picks, Amazon recommendations, the iTunes Genius button. They all have one thing in common: they are driven by clever algorithms that use a technique known as collaborative filtering. Often used in machine learning operations, collaborative filtering is the process by which a firm like Netflix generates predictions about a single user's preferences using data taken from a large number of users. This technical note offers an overview of three of the main collaborative filtering methods: slope one, a purely predictive nonparametric model; ordinal logit, a parametric regression model; and alternative least squares, a matrix factorization technique.