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Conference Papers Year : 2015

Similitude: Decentralised Adaptation in Large-Scale P2P Recommenders

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Abstract

Decentralised recommenders have been proposed to deliver privacy-preserving, personalised and highly scalable on-line recommendations. Current implementations tend, however, to rely on a hard-wired similarity metric that cannot adapt. This constitutes a strong limitation in the face of evolving needs. In this paper, we propose a framework to develop dynamically adaptive decentralised recommendation systems. Our proposal supports a decentralised 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 mission.
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Dates and versions

hal-01138365 , version 1 (02-04-2015)
hal-01138365 , version 2 (05-06-2015)

Licence

Attribution - CC BY 4.0

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Cite

Davide Frey, Anne-Marie Kermarrec, Christopher Maddock, Andreas Mauthe, Pierre-Louis Roman, et al.. Similitude: Decentralised Adaptation in Large-Scale P2P Recommenders. 15th IFIP International Conference on Distributed Applications and Interoperable Systems (DAIS), Jun 2015, Grenoble, France. pp.51-65, ⟨10.1007/978-3-319-19129-4_5⟩. ⟨hal-01138365v2⟩
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