The goal of this book is to bring together important research in a new family of recommender systems aimed at serving LBSNs. The chapters introduce a wide variety of recent approaches, from the most basic to the state-of-the-art, for providing recommendations in LBSNs. The material covered in the book is addressed to graduate students, teachers, researchers, and practitioners in the areas of web data mining, information retrieval, and machine learning. The book is organized into three parts. Part I provides introductory material on recommender systems, online social networks and LBSNs. Part II presents a wide variety of recommendation algorithms, ranging from the most basic methods to the state-of-the-art, as well as a comparison of the characteristics of these recommender systems. Part III provides a step-by-step case study on the technical aspects of deploying and evaluating a real-world LBSN, which provides location, activity and friend recommendations.