%0 Conference Proceedings
%T Similitude: Decentralised Adaptation in Large-Scale P2P Recommenders
%+ As Scalable As Possible: foundations of large scale dynamic distributed systems (ASAP)
%+ Computing Department
%A Frey, Davide
%A Kermarrec, Anne-Marie
%A Maddock, Christopher
%A Mauthe, Andreas
%A Roman, Pierre-Louis
%A Taïani, François
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 15th IFIP International Conference on Distributed Applications and Interoperable Systems (DAIS)
%C Grenoble, France
%Y Alysson Bessani
%Y Sara Bouchenak
%I Springer International Publishing
%3 Distributed Applications and Interoperable Systems
%V LNCS-9038
%P 51-65
%8 2015-06
%D 2015
%R 10.1007/978-3-319-19129-4_5
%K Adaptation
%K Recommendation Systems
%K Decentralised Systems
%K Distributed Computing
%K Collaborative Filtering
%Z Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC]
%Z Computer Science [cs]/Social and Information Networks [cs.SI]Conference papers
%X 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.
%G English
%Z TC 6
%Z WG 6.1
%2 https://inria.hal.science/hal-01138365v2/document
%2 https://inria.hal.science/hal-01138365v2/file/similitude_dais2015.pdf
%L hal-01138365
%U https://inria.hal.science/hal-01138365
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