  The advent of the Semantic Web necessitates paradigm shifts away from
centralized client/server architectures towards decentralization and
peer-to-peer computation, making the existence of central authorities
superfluous and even impossible. At the same time, recommender systems are
gaining considerable impact in e-commerce, providing people with
recommendations that are personalized and tailored to their very needs. These
recommender systems have traditionally been deployed with stark centralized
scenarios in mind, operating in closed communities detached from their host
network's outer perimeter. We aim at marrying these two worlds, i.e.,
decentralized peer-to-peer computing and recommender systems, in one
agent-based framework. Our architecture features an epidemic-style protocol
maintaining neighborhoods of like-minded peers in a robust, selforganizing
fashion. In order to demonstrate our architecture's ability to retain
scalability, robustness and to allow for convergence towards high-quality
recommendations, we conduct offline experiments on top of the popular MovieLens
dataset.
