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.
https://hal.inria.fr/hal-01138365 Contributor : Pierre-Louis RomanConnect in order to contact the contributor Submitted on : Friday, June 5, 2015 - 3:00:25 PM Last modification on : Thursday, January 20, 2022 - 4:19:58 PM Long-term archiving on: : Tuesday, April 25, 2017 - 3:20:54 AM
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⟩