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Heterogeneity, quality, and reputation in an adaptive recommendation model

Author

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  • G. Cimini

    ()

  • M. Medo
  • T. Zhou
  • D. Wei
  • Y.-C. Zhang

Abstract

Recommender systems help people cope with the problem of information overload. A recently proposed adaptive news recommender model [M. Medo, Y.-C. Zhang, T. Zhou, Europhys. Lett. 88, 38005 (2009)] is based on epidemic-like spreading of news in a social network. By means of agent-based simulations we study a “good get richer” feature of the model and determine which attributes are necessary for a user to play a leading role in the network. We further investigate the filtering efficiency of the model as well as its robustness against malicious and spamming behaviour. We show that incorporating user reputation in the recommendation process can substantially improve the outcome. Copyright EDP Sciences, SIF, Springer-Verlag Berlin Heidelberg 2011

Suggested Citation

  • G. Cimini & M. Medo & T. Zhou & D. Wei & Y.-C. Zhang, 2011. "Heterogeneity, quality, and reputation in an adaptive recommendation model," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 80(2), pages 201-208, March.
  • Handle: RePEc:spr:eurphb:v:80:y:2011:i:2:p:201-208
    DOI: 10.1140/epjb/e2010-10716-5
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    References listed on IDEAS

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    Cited by:

    1. Xu, Jinghong & Zhang, Lin & Ma, Baojun & Wu, Ye, 2016. "Impacts of suppressing guide on information spreading," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 922-927.
    2. Zhao, Narisa & Cui, Xuelian, 2017. "Impact of individual interest shift on information dissemination in modular networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 232-242.

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