Davide Frey, Anne-Marie Kermarrec, Christopher Maddock, Andreas Mauthe, Pierre-Louis Roman, and François Taïani

Similitude: Decentralised Adaptation in Large-Scale P2P Recommenders

Proceedings of the 15th IFIP WG 6.1 International Conference on Distributed Applications and Interoperable Systems (DAIS'15), Grenoble, France, 2-4 June, pp. 51-65, 2015 (14p.)

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.

complete document

doi:http://doi.org/10.1007/978-3-319-19129-4_5 (publisher's link)


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