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

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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
<mark>Original language</mark>English


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.