Similitude: Decentralised Adaptation in Large-Scale P2P Recommenders

  • Davide Frey
  • Anne-Marie Kermarrec
  • Christopher Maddock
  • Andreas Mauthe
  • Pierre-Louis Roman
  • François Taïani
Conference paper

DOI: 10.1007/978-3-319-19129-4_5

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9038)
Cite this paper as:
Frey D., Kermarrec AM., Maddock C., Mauthe A., Roman PL., Taïani F. (2015) Similitude: Decentralised Adaptation in Large-Scale P2P Recommenders. In: Bessani A., Bouchenak S. (eds) Distributed Applications and Interoperable Systems. DAIS 2015. Lecture Notes in Computer Science, vol 9038. Springer, Cham

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.

Keywords

Distributed Computing Decentralised Systems Collaborative Filtering Recommendation Systems Adaptation 

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Copyright information

© IFIP International Federation for Information Processing 2015

Authors and Affiliations

  • Davide Frey
    • 1
  • Anne-Marie Kermarrec
    • 1
  • Christopher Maddock
    • 2
  • Andreas Mauthe
    • 2
  • Pierre-Louis Roman
    • 3
  • François Taïani
    • 3
  1. 1.InriaRennesFrance
  2. 2.School of Computing and CommunicationsLancaster UniversityLancasterUK
  3. 3.Université de Rennes 1, IRISA - ESIRRennesFrance

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