Similitude: Decentralised Adaptation in Large-Scale P2P Recommenders

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.
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Alysson Bessani; Sara Bouchenak. 15th IFIP International Conference on Distributed Applications and Interoperable Systems (DAIS), Jun 2015, Grenoble, France. Springer International Publishing, Lecture Notes in Computer Science, LNCS-9038, pp.51-65, 2015, Distributed Applications and Interoperable Systems. 〈10.1007/978-3-319-19129-4_5〉
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Davide Frey, Anne-Marie Kermarrec, Christopher Maddock, Andreas Mauthe, Pierre-Louis Roman, et al.. Similitude: Decentralised Adaptation in Large-Scale P2P Recommenders. Alysson Bessani; Sara Bouchenak. 15th IFIP International Conference on Distributed Applications and Interoperable Systems (DAIS), Jun 2015, Grenoble, France. Springer International Publishing, Lecture Notes in Computer Science, LNCS-9038, pp.51-65, 2015, Distributed Applications and Interoperable Systems. 〈10.1007/978-3-319-19129-4_5〉. 〈hal-01138365v2〉

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