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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.
Cited literature [28 references]
https://hal.inria.fr/hal-01138365
Contributor : Pierre-Louis Roman <>
Submitted on : Friday, June 5, 2015 - 3:00:25 PM
Last modification on : Friday, July 10, 2020 - 4:16:23 PM
Long-term archiving on: : Tuesday, April 25, 2017 - 3:20:54 AM
Contributor : Pierre-Louis Roman <>
Submitted on : Friday, June 5, 2015 - 3:00:25 PM
Last modification on : Friday, July 10, 2020 - 4:16:23 PM
Long-term archiving on: : Tuesday, April 25, 2017 - 3:20:54 AM
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similitude_dais2015.pdf
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Distributed under a Creative Commons Attribution 4.0 International License