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Adaptation for the masses: towards decentralised adaptation in large-scale P2P recommenders

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paper

Publication date10/2014
Host publicationARM '14 Proceedings of 13th Workshop on Adaptive and Reflective Middleware
Place of PublicationNew York
Number of pages6
ISBN (Print)9781450332323
Original languageEnglish


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 paper, propose a framework to develop dynamically adaptive decentralized recommendation systems. Our proposal supports a decentralized form of adaptation, in which individual nodes can independently select, and update their own recommendation algorithm, while still collectively contributing to the overall system's services.