Home > Research > Publications & Outputs > Similitude

Electronic data

  • similitude_dais2015

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

    Accepted author manuscript, 1.65 MB, PDF document

    Available under license: CC BY: Creative Commons Attribution 4.0 International License

Links

Text available via DOI:

View graph of relations

Similitude: decentralised adaptation in large-scale P2P recommenders

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Published
Close
Publication date06/2015
Host publicationDistributed Applications and Interoperable Systems: 15th IFIP WG 6.1 International Conference, DAIS 2015, Held as Part of the 10th International Federated Conference on Distributed Computing Techniques, DisCoTec 2015, Grenoble, France, June 2-4, 2015, Proceedings
EditorsAlysson Bessani, Sara Bounchenak
PublisherSpringer
Pages51-65
Number of pages15
ISBN (electronic)9783319191294
ISBN (print)9783319191287
<mark>Original language</mark>English

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume9038
ISSN (Print)0302-9743

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

Bibliographic note

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