Highly Recommended: Collaborative Filtering Gives Customers What They Want

Abstract

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. Excerpt UVA-M-0974 Aug. 23, 2019 Highly Recommended: Collaborative Filtering Gives Customers What They Want You're ready to Netflix and chill. You pull up your browser and scroll through the new releases on Netflix.com. Nothing looks interesting. You turn your attention to the recommended titles, the “Top Picks” selected just for you. And there it is—the classic film you'd forgotten you wanted to see but has always been at the top of your to-watch list. 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. . . .