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.
CITATION STYLE
Frey, D., Kermarrec, A. M., Maddock, C., Mauthe, A., Roman, P. L., & Taïani, F. (2015). Similitude: Decentralised adaptation in large-scale P2P recommenders. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9038, pp. 51–65). Springer Verlag. https://doi.org/10.1007/978-3-319-19129-4_5
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