Davide Frey, Anne-Marie Kermarrec, Christopher Maddock, Andreas Mauthe, and François Taïani

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

Proceedings of the 13th Workshop on Adaptive and Reflective Middleware (ARM'14), Bordeaux, France, pp. 4:1-4:6, ISBN 978-1-4503-3232-3, ACM, New York, NY, USA, 2014 (6p.)

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.

ACM Copyright Notice: © ACM, 2014. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceedings of the 13th Workshop on Reflective and Adaptive Middleware (ARM 2014, Bordeaux, France, December 9, 2014)..

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doi:http://doi.org/10.1145/2677017.2677021 (publisher's link)


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