Decentralized recommenders have been proposed to deliver privacy-preserving, personalized and highly scalable on-line recommendation services. Current implementations tend, however, to rely on hard-wired, mechanisms that cannot adapt. Deciding beforehand which hard-wired mechanism to use can be difficult, as the optimal choice might depend on conditions that are unknown at design time. In this pa-per, propose a framework to develop dynamically adaptive decentralized recommendation systems. Our proposal sup-ports a decentralized form of adaptation, in which individual nodes can independently select, and update their own rec-ommendation algorithm, while still collectively contributing to the overall system's services.