This document provides an introduction to recommender systems. It defines recommenders as information filtering systems that seek to predict a user's preferences for items. It describes different types of recommender systems, including content-based, collaborative filtering, and hybrid approaches. The history of recommenders is discussed from early manual filtering to modern automated approaches. Popular algorithms and open-source tools like Lenskit and Mahout are also introduced.