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  • similitude_dais2015

    Rights statement: The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-19129-4_5

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Similitude: decentralised adaptation in large-scale P2P recommenders

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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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The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-19129-4_5