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Adaptation for the masses: towards decentralised adaptation in large-scale P2P recommenders

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Adaptation for the masses : towards decentralised adaptation in large-scale P2P recommenders. / Frey, Davide; Kermarrec, Anne-Marie; Maddock, Christopher; Mauthe, Andreas; Taïani, Francois.

ARM '14 Proceedings of 13th Workshop on Adaptive and Reflective Middleware. New York : ACM, 2014. 4.

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

Harvard

Frey, D, Kermarrec, A-M, Maddock, C, Mauthe, A & Taïani, F 2014, Adaptation for the masses: towards decentralised adaptation in large-scale P2P recommenders. in ARM '14 Proceedings of 13th Workshop on Adaptive and Reflective Middleware., 4, ACM, New York. https://doi.org/10.1145/2677017.2677021

APA

Frey, D., Kermarrec, A-M., Maddock, C., Mauthe, A., & Taïani, F. (2014). Adaptation for the masses: towards decentralised adaptation in large-scale P2P recommenders. In ARM '14 Proceedings of 13th Workshop on Adaptive and Reflective Middleware [4] New York: ACM. https://doi.org/10.1145/2677017.2677021

Vancouver

Frey D, Kermarrec A-M, Maddock C, Mauthe A, Taïani F. Adaptation for the masses: towards decentralised adaptation in large-scale P2P recommenders. In ARM '14 Proceedings of 13th Workshop on Adaptive and Reflective Middleware. New York: ACM. 2014. 4 https://doi.org/10.1145/2677017.2677021

Author

Frey, Davide ; Kermarrec, Anne-Marie ; Maddock, Christopher ; Mauthe, Andreas ; Taïani, Francois. / Adaptation for the masses : towards decentralised adaptation in large-scale P2P recommenders. ARM '14 Proceedings of 13th Workshop on Adaptive and Reflective Middleware. New York : ACM, 2014.

Bibtex

@inproceedings{e8aadd17be90471aa047de4eb75be2f8,
title = "Adaptation for the masses: towards decentralised adaptation in large-scale P2P recommenders",
abstract = "Decentralized recommenders have been proposed to deliver privacy-preserving, personalized and highly scalable on-line recommendation services. Current implementations tend, however, to rely on hard-wired, mechanisms that cannot adapt. Deciding beforehand which hard-wired mechanism to use can be difficult, as the optimal choice might depend on conditions that are unknown at design time. In this paper, propose a framework to develop dynamically adaptive decentralized recommendation systems. Our proposal supports a decentralized 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 services.",
author = "Davide Frey and Anne-Marie Kermarrec and Christopher Maddock and Andreas Mauthe and Francois Ta{\"i}ani",
year = "2014",
month = "10",
doi = "10.1145/2677017.2677021",
language = "English",
isbn = "9781450332323",
booktitle = "ARM '14 Proceedings of 13th Workshop on Adaptive and Reflective Middleware",
publisher = "ACM",

}

RIS

TY - GEN

T1 - Adaptation for the masses

T2 - towards decentralised adaptation in large-scale P2P recommenders

AU - Frey, Davide

AU - Kermarrec, Anne-Marie

AU - Maddock, Christopher

AU - Mauthe, Andreas

AU - Taïani, Francois

PY - 2014/10

Y1 - 2014/10

N2 - Decentralized recommenders have been proposed to deliver privacy-preserving, personalized and highly scalable on-line recommendation services. Current implementations tend, however, to rely on hard-wired, mechanisms that cannot adapt. Deciding beforehand which hard-wired mechanism to use can be difficult, as the optimal choice might depend on conditions that are unknown at design time. In this paper, propose a framework to develop dynamically adaptive decentralized recommendation systems. Our proposal supports a decentralized 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 services.

AB - Decentralized recommenders have been proposed to deliver privacy-preserving, personalized and highly scalable on-line recommendation services. Current implementations tend, however, to rely on hard-wired, mechanisms that cannot adapt. Deciding beforehand which hard-wired mechanism to use can be difficult, as the optimal choice might depend on conditions that are unknown at design time. In this paper, propose a framework to develop dynamically adaptive decentralized recommendation systems. Our proposal supports a decentralized 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 services.

U2 - 10.1145/2677017.2677021

DO - 10.1145/2677017.2677021

M3 - Conference contribution/Paper

SN - 9781450332323

BT - ARM '14 Proceedings of 13th Workshop on Adaptive and Reflective Middleware

PB - ACM

CY - New York

ER -