Personalized recommendation of products, documents, and collaborators has become an important way of meeting user needs in commerce, information provision, and community services, whether on the web, through mobile interfaces, or through traditional desktop interfaces. This tutorial first reviews the types of personalized recommendation that are being used commercially and in research systems. It then systematically presents and compares the underlying AI techniques, including recent variants and extensions of collaborative filtering, demographic and case-based approaches, and decision-theoretic methods. The properties of the various techniques will be compared within a general framework, so that participants learn how to match recommendation techniques to applications and how to combine complementary techniques.