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Similitude: Decentralised Adaptation in Large-Scale P2P Recommenders

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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https://hal.inria.fr/hal-01138365
Contributor : Pierre-Louis Roman <>
Submitted on : Thursday, April 2, 2015 - 2:19:09 PM
Last modification on : Monday, December 3, 2018 - 10:20:04 PM
Long-term archiving on: : Tuesday, April 18, 2017 - 7:47:22 AM

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Davide Frey, Anne-Marie Kermarrec, Christopher Maddock, Andreas Mauthe, Pierre-Louis Roman, et al.. Similitude: Decentralised Adaptation in Large-Scale P2P Recommenders. DAIS 2015 - 15th IFIP International Conference on Distributed Applications and Interoperable Systems, Jun 2015, Grenoble, France. ⟨10.1007/978-3-319-19129-4_5⟩. ⟨hal-01138365v1⟩

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