Similitude: Decentralised Adaptation in Large-Scale P2P Recommenders - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2015

Similitude: Decentralised Adaptation in Large-Scale P2P Recommenders


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.
Fichier principal
Vignette du fichier
similitude_dais2015.pdf (1.17 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

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





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⟩
672 View
311 Download



Gmail Facebook X LinkedIn More