Automated recommender systems make product suggestions that are tailored to the individual needs of the user and represent powerful means to combat information glut. However, their practical applicability has been largely confined to scenarios where information relevant for recommendation making is kept in one single, authoritative node. Recently, novel distributed infrastructures are emerging, e.g., peer-to-peer networks and the Semantic Web, which could likewise benefit from recommender system services, leading to a paradigm shift towards decentralized recommender systems. In this book, we investigate the challenges that decentralized recommenders bring up and propose techniques to cope with those issues. The spectrum ranges from the use of product classification taxonomies, alleviating the sparsity problem, to trust propagation mechanisms designed to address the scalability issue. Empirical investigations on the correlation of interpersonal trust and interest similarity provide the component glue that melds these results. The book is geared towards academic readers and practitioners alike, with a focus on both implementable algorithms as well as new socio-psychological insights.