Adaptation for the Masses: Towards Decentralized Adaptation in Large-Scale P2P Recommenders

Abstract : 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.
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Davide Frey, Anne-Marie Kermarrec, Christopher Maddock, Andreas Mauthe, François Taïani. Adaptation for the Masses: Towards Decentralized Adaptation in Large-Scale P2P Recommenders. Workshop on Adaptive and Reflective Middleware ARM 2014, Dec 2014, Bordeaux, France. ⟨10.1145/2677017.2677021⟩. ⟨hal-01080030⟩

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