Recommender systems have become ubiquitous online, and have recently started to become increasingly more integrated into our everyday, physical, offline aspects our lives. In this talk, I will talk about what recommender systems actually are, how they work, and how they can be applied in various settings, online and offline. Starting with a short historical recap of recommender systems research and industry applications, I will describe how the models used in recommender systems have developed and progressed with the evolution of machine learning systems in the last two decades. While recommender systems are driven by underlying machine learning models, they also rely on interaction design and engineering, usability and user experience, and cognitive science theories to provide well-functioning solutions to complex information tasks. I will discuss how this affects the development and application of recommender systems.