Similitude:decentralised adaptation in large-scale P2P recommenders

Frey, David and Kermarrec, Anne-Marie and Maddock, Christopher and Mauthe, Andreas Ulrich and Roman, Pierre-Louis and Taiani, Francois (2015) Similitude:decentralised adaptation in large-scale P2P recommenders. In: Distributed Applications and Interoperable Systems. Lecture Notes in Computer Science . Springer, pp. 51-65. ISBN 9783319191287

PDF (similitude_dais2015)
similitude_dais2015.pdf - Accepted Version
Available under License Creative Commons Attribution.

Download (1MB)


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. Keywords

Item Type: Contribution in Book/Report/Proceedings
Additional Information: The final publication is available at Springer via
Departments: Faculty of Science and Technology > School of Computing & Communications
ID Code: 78012
Deposited By: ep_importer_pure
Deposited On: 28 Jan 2016 13:20
Refereed?: Yes
Published?: Published
Last Modified: 18 Feb 2020 05:16

Actions (login required)

View Item View Item