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Distributed user profiling via spectral methods

Authors:

Dan-Cristian Tomozei,

Laurent MassouliéAuthors Info & Claims

ACM SIGMETRICS Performance Evaluation Review, Volume 38, Issue 1

Pages 383 - 384

https://doi.org/10.1145/1811099.1811098

Published: 14 June 2010 Publication History

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Abstract

User profiling is a useful primitive for constructing personalized services, such as content recommendation. In the present work we investigate the feasibility of user profiling in a distributed setting, with no central authority and only local information exchanges between users. Our main contributions are: (i)~We propose a spectral clustering technique, and prove its ability to recover unknown user profiles with only few measures of affinity between users. (ii)~We develop distributed algorithms which achieve an embedding of users into a low-dimensional space, based on spectral transformation. These involve simple message passing among users, and provably converge to the desired embedding.

References

[1]

V.Borkar and S.P.Meyn. Oja's Algorithm for Graph Clustering and Markov Spectral Decomposition. In ValueTools'08, pages 1--7, Brussels, 2008. ICST.

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[2]

E. Oja and J. Karhunen. On Stochastic Approximation of the Eigenvectors and Eigenvalues of the Expectation of a Random Matrix. Journal of Math. An. and App., 106(1), 1985.

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Cited By

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Zhou ZAmini A(2019)Analysis of spectral clustering algorithms for community detectionThe Journal of Machine Learning Research10.5555/3322706.336198820:1(1774-1820)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.5555/3322706.3361988
Barforoush AShirazi HEmami H(2017)A New Classification Framework to Evaluate the Entity Profiling on the WebACM Computing Surveys10.1145/306690450:3(1-39)Online publication date: 29-Jun-2017
https://dl.acm.org/doi/10.1145/3066904
Morral GBianchi P(2016)Distributed on-line multidimensional scaling for self-localization in wireless sensor networksSignal Processing10.1016/j.sigpro.2015.08.014120:C(88-98)Online publication date: 1-Mar-2016
https://dl.acm.org/doi/10.1016/j.sigpro.2015.08.014
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Index Terms

Distributed user profiling via spectral methods
1. General and reference
  1. Cross-computing tools and techniques
    1. Performance
2. Software and its engineering
  1. Software organization and properties
    1. Software system structures
      1. Distributed systems organizing principles
        Organizing principles for web applications

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Information & Contributors

Information

Published In

ACM SIGMETRICS Performance Evaluation Review Volume 38, Issue 1

Performance evaluation review

June 2010

382 pages

ISSN:0163-5999

DOI:10.1145/1811099

Issue’s Table of Contents

SIGMETRICS '10: Proceedings of the ACM SIGMETRICS international conference on Measurement and modeling of computer systems
June 2010
398 pages
ISBN:9781450300384
DOI:10.1145/1811039
General Chair:
Vishal Misra
Columbia University, USA
,
Program Chairs:
Paul Barford
University of Wisconsin-Madison, USA
,
Mark Squillante
IBM T.J. Watson Research Center, USA

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 June 2010

Published in SIGMETRICS Volume 38, Issue 1

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Cited By

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Zhou ZAmini A(2019)Analysis of spectral clustering algorithms for community detectionThe Journal of Machine Learning Research10.5555/3322706.336198820:1(1774-1820)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.5555/3322706.3361988
Barforoush AShirazi HEmami H(2017)A New Classification Framework to Evaluate the Entity Profiling on the WebACM Computing Surveys10.1145/306690450:3(1-39)Online publication date: 29-Jun-2017
https://dl.acm.org/doi/10.1145/3066904
Morral GBianchi P(2016)Distributed on-line multidimensional scaling for self-localization in wireless sensor networksSignal Processing10.1016/j.sigpro.2015.08.014120:C(88-98)Online publication date: 1-Mar-2016
https://dl.acm.org/doi/10.1016/j.sigpro.2015.08.014
Tomozei DMassoulié L(2014)Distributed user Profiling via Spectral MethodsStochastic Systems10.1287/11-SSY0364:1(1-43)Online publication date: Jun-2014
https://doi.org/10.1287/11-SSY036
Xu JWu RZhu KHajek BSrikant RYing L(2014)Jointly clustering rows and columns of binary matricesACM SIGMETRICS Performance Evaluation Review10.1145/2637364.259200542:1(29-41)Online publication date: 16-Jun-2014
https://dl.acm.org/doi/10.1145/2637364.2592005
Xu JWu RZhu KHajek BSrikant RYing LSanghavi SShakkottai SLelarge MSchroeder B(2014)Jointly clustering rows and columns of binary matricesThe 2014 ACM international conference on Measurement and modeling of computer systems10.1145/2591971.2592005(29-41)Online publication date: 16-Jun-2014
https://dl.acm.org/doi/10.1145/2591971.2592005
Rosà AChen LBinder WToth MFritchie S(2016)Profiling actor utilization and communication in AkkaProceedings of the 15th International Workshop on Erlang10.1145/2975969.2975972(24-32)Online publication date: 23-Sep-2016
https://dl.acm.org/doi/10.1145/2975969.2975972
Karydi EMargaritis K(2016)Parallel and Distributed Collaborative FilteringACM Computing Surveys10.1145/295195249:2(1-41)Online publication date: 13-Aug-2016
https://dl.acm.org/doi/10.1145/2951952
Morral GDieng NBianchi P(2014)Distributed on-line multidimensional scaling for self-localization in wireless sensor networks2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP.2014.6853769(1110-1114)Online publication date: May-2014
https://doi.org/10.1109/ICASSP.2014.6853769
Moreno ACastro HRiveill M(2014)Client-Side Hybrid Rating Prediction for RecommendationUser Modeling, Adaptation, and Personalization10.1007/978-3-319-08786-3_33(369-380)Online publication date: 2014
https://doi.org/10.1007/978-3-319-08786-3_33
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