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User Recommendation in Low Degree Networks with a Learning-Based Approach

  • Marcelo G. ArmentanoEmail author
  • Ariel Monteserin
  • Franco Berdun
  • Emilio Bongiorno
  • Luis María Coussirat
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11288)

Abstract

User recommendation plays an important role in microblogging systems since users connect to these networks to share and consume content. Finding relevant users to follow is then a hot topic in the study of social networks. Microblogging networks are characterized by having a large number of users, but each of them connects with a limited number of other users, making the graph of followers to have a low degree. One of the main problems of approaching user recommendation with a learning-based approach in low-degree networks is the problem of extreme class imbalance. In this article, we propose a balancing scheme to face this problem, and we evaluate different classification algorithms using as features classical metrics for link prediction. We found that the learning-based approach outperformed individual metrics for the problem of user recommendation in the evaluated dataset. We also found that the proposed balancing approach lead to better results, enabling a better identification of existing connections between users.

Keywords

User recommendation Online social networks Link prediction 

Notes

Acknowledgements

This work was partially supported by research project PICT-2014-2750.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Marcelo G. Armentano
    • 1
  • Ariel Monteserin
    • 1
  • Franco Berdun
    • 1
  • Emilio Bongiorno
    • 2
  • Luis María Coussirat
    • 2
  1. 1.ISISTAN Research Institute (CONICET-UNICEN)TandilArgentina
  2. 2.Facultad de Ciencias ExactasUNICENTandilArgentina

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