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Similitude: Decentralised Adaptation in Large-Scale P2P Recommenders

Davide Frey 1, * Anne-Marie Kermarrec 1 Christopher Maddock 2 Andreas Mauthe 2 Pierre-Louis Roman 1 François Taïani 1 
* Corresponding author
1 ASAP - As Scalable As Possible: foundations of large scale dynamic distributed systems
Inria Rennes – Bretagne Atlantique , IRISA-D1 - SYSTÈMES LARGE ÉCHELLE
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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Submitted on : Friday, June 5, 2015 - 3:00:25 PM
Last modification on : Thursday, January 20, 2022 - 4:19:58 PM
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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. 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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