Similitude: Decentralised Adaptation in Large-Scale P2P Recommenders - INRIA - Institut National de Recherche en Informatique et en Automatique Accéder directement au contenu
Communication Dans Un Congrès Année : 2015

Similitude: Decentralised Adaptation in Large-Scale P2P Recommenders

Résumé

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
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

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

Licence

Paternité

Identifiants

Citer

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⟩
667 Consultations
309 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More